1. Introduction

1.1. Pose research questions

In today’s medical field, the early diagnosis and treatment of breast cancer is of great significance to improve the survival rate of patients. As the incidence of breast cancer increases year by year, how to use modern technology to improve its prediction accuracy has become an urgent problem to be solved. With the rapid development of artificial intelligence and machine learning technologies, the application of these technologies to the prediction and diagnosis of breast cancer has proven to be an effective aid. Machine learning works by building mathematical models that enable computers to learn from data and make predictions or decisions, rather than being explicitly programmed to perform tasks. In the breast cancer prediction scenario, machine learning can help us extract valuable information from large amounts of medical data in order to more accurately predict the risk of breast cancer.

However, although machine learning techniques have made some progress in breast cancer prediction, there are still some limitations and challenges with current prediction systems. For example, existing predictive models may not work well when dealing with unstructured medical data, and there are differences in predictive results between different models, which leads to uncertainty in diagnosis. In addition, the complexity of data preprocessing, feature selection, model construction and parameter tuning also put forward higher requirements for the design and implementation of the prediction system. Therefore, this study aims to explore the design and implementation of a machine learning-based breast cancer prediction system, and propose an improvement scheme for existing problems.

Firstly, this study will summarize the application status and existing problems of machine learning in breast cancer detection through literature review and theoretical analysis. Then, the research will focus on the design and implementation of breast cancer prediction system, including the design of system architecture, data preprocessing and feature selection methods, machine learning model construction and system testing and other key links. In addition, this study will also discuss the problems encountered in the breast cancer prediction system and propose corresponding solutions to improve the accuracy and practicability of the prediction system.

Research on breast cancer prediction system can not only provide auxiliary decision support for the medical industry, but also have important value for promoting the application of machine learning technology in the medical and health field. The significance of this study is that it can not only promote the development of breast cancer prediction technology, but also provide reference and inspiration for researchers in related fields. Through in-depth research on breast cancer prediction systems, This research can better understand the application potential and challenges of machine learning technology in the medical field, laying the foundation for future innovation and advancement of related technologies.

1.2. Research value and significance

Breast cancer is one of the most common cancer types in women worldwide, and its early diagnosis and treatment are of vital importance to improve the survival rate of patients. With the deepening of the application of artificial intelligence technology, especially machine learning in the medical field, the design and implementation of breast cancer prediction system based on machine learning has become a hot research direction in the current medical and health data analysis. This study aims to explore and implement an efficient and accurate breast cancer prediction system that will not only help physicians make a diagnosis faster in clinical decision making, but also provide patients with a personalized risk assessment, leading to early intervention and treatment.

The value of research is mainly reflected in the following aspects:

First of all, the accuracy of breast cancer prediction can be effectively improved through the construction and application of machine learning models. Although traditional breast cancer detection methods such as Mammography have been widely used, there are still problems of misdiagnosis and missed diagnosis, and machine learning algorithms can learn from massive medical data and extract complex patterns conducive to cancer prediction, which is of great significance for improving the accuracy of diagnosis.

Secondly, the application of machine learning algorithms can significantly improve the efficiency of breast cancer screening. Using algorithms to automatically analyze medical images or biomarker data can reduce the workload of doctors, increase the speed of screening, and enable more patients to be diagnosed in less time.

Third, a machine learning-based breast cancer prediction system could support personalized medicine. By analyzing multi-dimensional data such as an individual patient’s medical history, genetic information, and lifestyle, machine learning models can provide a customized risk assessment for each patient and help doctors develop a more precise treatment plan.

In addition, the study has important scientific significance. It not only promotes the applied research of machine learning in the field of breast cancer prediction, but also promotes the exchange and integration of interdisciplinary knowledge, and provides referential experience and technical routes for the prediction and diagnosis of other types of cancer in the future.

Finally, the realization of such a prediction system can also promote the collection and analysis of relevant medical big data, provide data support for future clinical research and public health decision-making, and have long-term social and economic benefits.

In summary, the design and implementation of the machine learning-based breast cancer prediction system can not only improve the diagnostic efficiency and accuracy of breast cancer, reduce medical costs, but also promote the development of personalized medicine, enhance the response ability of the public health system, and lay the foundation for further research and application of machine learning technology in the medical field. Therefore, this study has important theoretical value and practical significance.

1.3. Article structure logic

This paper aims to design and implement an efficient breast cancer prediction system through machine learning technology to improve the early detection and diagnosis rate of breast cancer. In order to ensure that the research is organized and coherent, the structure of the paper is carefully planned to guide the reader through the various aspects of the research step by step.

Firstly, the introduction sets the background and tone of the research. In this section, This research first ask the research question of how machine learning can be used to predict breast cancer, as well as the advantages and possible challenges of this technique compared to traditional methods. It then elaborates on the value and significance of research, including potential contributions to patients, healthcare systems, and scientific research. Finally, the paper Outlines the structure and logic of the article, provides readers with a blueprint for the overall picture of the study, and clearly points out the main content and function of each part.

The literature review provides the necessary theoretical support and research background for the research. This part first reviews the theoretical basis of machine learning in breast cancer detection, including the basic concepts, principles and applications of machine learning in medical image analysis. Then, the common machine learning algorithms and their advantages and disadvantages are discussed, which lays a foundation for the subsequent selection of suitable algorithms. In addition, the latest research progress and the development trend of related technologies in breast cancer prediction system at home and abroad are introduced, and the research hotspot and future potential in this field are demonstrated.

System design and implementation is the core of the paper, which describes the development process of the prediction system in detail. This part starts with the system architecture design and explains the overall framework, data flow and processing flow of the prediction system. Then, the methods and techniques of data preprocessing and feature selection are introduced, and their importance in breast cancer prediction is emphasized. In the Machine learning model building section, the process of selecting the appropriate machine learning algorithm, model training and parameter tuning is explained in detail. Finally, in the section of System implementation and testing, the selection of the actual development environment and tools, as well as the system test method and result analysis are described.

The discussion part makes an in-depth analysis of the research results, and probes into the possible problems and their causes. In this part, the applicability of the machine learning model in breast cancer prediction is analyzed, and the reasons for the inefficiency of the model are discussed, as well as possible solutions, and improvement suggestions and directions are provided for subsequent research.

Finally, the summary part synthesizes the whole paper, reviews the research questions and research methods, summarizes the research results and findings. At the same time, it points out the limitations of the research, and puts forward the prospect of the future research direction, which provides the thought and possible research path for the scholars in this field.

2. Literature Review

2.1. Theoretical review

As a method of data analysis, machine learning learns from data and makes judgments or predictions by building models. In the field of breast cancer detection, machine learning technology is widely used in the analysis and interpretation of medical images, such as mammography, ultrasound and MRI scans. Recent studies have shown that machine learning methods can effectively improve the accuracy and efficiency of breast cancer detection. For example, Scholar [1] study showed that deep learning algorithms are able to automatically detect small changes in malignancies in mammogram images that might otherwise be missed by radiologists. In addition, Scholar [2] achieved the distinction between benign and malignant tumors by using convolutional neural networks (CNN) to classify ultrasound images of the breast with a significantly higher accuracy than traditional image processing methods.

Common machine learning algorithms include logistic regression, support vector machines (SVM), random forests, and neural networks. Each of these algorithms has advantages and disadvantages. For example, Logistic Regression is a simple but powerful classification algorithm that is widely used in binary classification problems. Its main advantage is that the model is simple, easy to understand and implement, but its performance may be limited when the feature space is large or there are complex nonlinear relationships between features. Support vector machine (SVM) is a more complex algorithm that distinguishes different classes of data by constructing a hyperplane with maximum interval separation, which is suitable for the classification of high-dimensional data. SVM performs well on small sample datasets, but the choice of parameters and kernel function may affect the model’s performance. Random Forest is an ensemble learning algorithm that improves the overall classification accuracy by building multiple decision trees and combining their predictions. Random forest can effectively handle high-dimensional data and has good anti-overfitting ability, but its model interpretation is poor and training speed is slow on large-scale data sets. Neural networks, especially convolutional neural networks (CNNS) in deep learning, have made breakthroughs in image recognition and classification tasks due to their powerful feature extraction capabilities. CNN can automatically learn hierarchical feature representation from image data, but its model is complex and has many parameters, which requires a lot of data and computing resources for training.

In recent years, deep learning, especially CNN, has become a research hotspot in the field of breast cancer detection. Scholar [3] studied a CNN-based breast cancer prediction model that was able to automatically identify malignant tumor features from pathological images of breast tissue and achieved higher accuracy than traditional methods on multiple datasets. In addition, Scholar [4] propose a new deep learning framework that combines CNNS and recurrent neural networks (RNNS) to analyze patients’ historical medical records and breast image data, achieving dynamic prediction of breast cancer risk. These studies not only demonstrate the great potential of machine learning technology in the field of breast cancer detection, but also provide new ideas and methods for future research.

2.2. Latest research progress

In the field of breast cancer prediction system research, machine learning technology has made remarkable progress in recent years. With the rapid development of big data and computing power, researchers are able to use more complex and efficient algorithms to diagnose and predict breast cancer. Recent research has focused not only on improving the performance of algorithms themselves, but also increasingly on how to integrate multi-source data, improve model generalization, and interpret model predictions.

In 2021, Scholar [5] proposed a deep learning-based breast cancer detection framework that utilizes transfer learning techniques to extract deep features of breast tissue images through a pre-trained neural network model, which is then combined with patient clinical information to enhance the predictive accuracy of the model. This work shows that combining medical image data with patients’ clinical data can effectively improve the accuracy of breast cancer diagnosis.

Subsequently in 2018, Scholar [6] developed a breast cancer prediction model based on an improved random forest algorithm, which reduces the impact of dimensional curses and improves the model’s predictive performance by introducing a feature selection mechanism. In addition, they used cross-validation methods to evaluate the stability and reliability of the model, allowing the model to maintain high accuracy across different data sets.

In 2022, Scholar [7] broke through the limitations of a single algorithm and built a multi-modal fusion framework, which integrated image data and gene expression data, used CNN and graph Convolutional network (GCN) to process the two types of data respectively, and then fused their feature representations to achieve high-precision prediction of breast cancer subtypes. This study demonstrates the great potential of multimodal data fusion to improve the accuracy of breast cancer prediction.

In 2022, Scholar [7] proposed a dynamic feature selection strategy based on reinforcement learning that is able to select the subset of features that are most helpful for breast cancer prediction based on the characteristics of different patients, thus providing a personalized diagnosis for each patient. This method not only improves the accuracy of prediction, but also provides more precise support for clinical decision making.

Most recently in 2022, Scholar [7] collaborated on a method for early breast cancer prediction based on a combination of image processing and deep learning. They first used digital image processing to enhance the quality of the mammogram images, and then used deep learning models to learn and classify the features of the enhanced images. The experimental results show that this method can effectively improve the sensitivity and specificity of early detection of breast cancer.

These advances not only demonstrate the potential of machine learning in breast cancer prediction systems, but also point the way to future research, including how to further integrate different types of medical data, improve the explanatory power of models, and how to use medical big data while protecting patient privacy. As the technology continues to evolve, machine learning is expected to play a more important role in the early detection, diagnosis and treatment of breast cancer.

3. System Design and Implementation

3.1. System architecture design

When designing a machine learning-based breast cancer prediction system, the design of the system architecture is a crucial step, which is directly related to the effectiveness of subsequent model training and the accuracy of prediction results. The breast cancer prediction system architecture proposed in this study aims to achieve efficient and accurate prediction of breast cancer risk through advanced machine learning technology.

First of all, in the overall framework, the system adopts a common three-layer architecture pattern, including data layer, model layer and application layer. The data layer is responsible for collecting and storing medical data related to breast cancer, including but not limited to patients’ clinical test results, pathology reports, genetic information, and personal lifestyle habits. The model layer is the core of the system and is responsible for the construction, training and optimization of machine learning models. The application layer provides a user interface that allows doctors and researchers to easily enter data, obtain predictions, and analyze them accordingly.

In terms of data flow and processing process, the raw data is first collected from multiple channels through the data acquisition module, and then the data is fed into the data preprocessing module. In the data preprocessing module, a series of methods, such as missing value processing, outlier removal, data standardization, etc. are used to ensure the quality and consistency of data. The processed data is fed into a feature selection module, which uses algorithms such as random forest and recursive feature elimination (RFE) to identify and select the most influential features for breast cancer prediction.

After feature selection, the selected feature set is used to train the machine learning model. In this study, This research consider using a variety of machine learning algorithms and evaluate their performance through cross-validation. These algorithms may include support vector machines (SVM), decision trees, random forests, gradient lift trees (GBDT), and neural networks. Each algorithm has its own characteristics, such as SVM performs well on high-dimensional data, while random forest is more effective on data with complex relationships. By comparing the performance of different algorithms, This research can choose the model that best fits this prediction system.

After model selection, model training and parameter tuning are entered. In this stage, This research use grid search, Bayesian optimization and other methods, combined with cross-validation to find the optimal model parameters. In addition, in order to avoid overfitting the model, This research will also use regularization techniques such as L1, L2 regularization or Dropout.

Finally, in the system implementation and testing, This research will choose the appropriate development environment and tools, such as Python programming language with Scikit-learn, TensorFlow and other machine learning libraries to carry out actual coding work. After the system is developed, it will pass a series of tests, including unit tests, integration tests and system tests, to ensure the stability and reliability of the system. Testing includes not only code-level testing, but also evaluation of model performance, such as evaluating the predictive effectiveness of the model by calculating metrics such as confusion matrix, accuracy, recall, F1 score, and area under ROC curve (AUC).

The breast cancer prediction system architecture proposed in this study aims to provide scientific and effective decision support tools for early detection and treatment of breast cancer through well-designed data processing processes and advanced machine learning techniques. Through continuous testing and optimization, This research expect this system to play an important role in practical applications, ultimately helping to improve the survival and quality of life of breast cancer patients.”

3.2. Data preprocessing and feature selection

Data preprocessing includes data cleaning, missing value processing, outlier detection and standardization. Assume that our dataset contains clinical information and biomarker measurements from 100 patients, each with 30 characteristics such as tumor size, shape, density, and nuclear characteristics.

In the data cleaning phase, the study first excluded those samples whose records were incomplete or misformatted. For example, if the tumor size of the sample is negative, the sample is removed. For missing values, this study adopts the mean interpolation method, that is, the mean value of the same feature replaces the missing value. For example, if feature A has A missing value in 10 samples, this study calculates the mean of feature A in the remaining 9 samples and fills in the missing value with that mean.

Detection of outliers typically uses a boxplot to identify data points that are more than 1.5 quartile distances away. For example, if the third quartile of feature B is Q3 and the first quartile is Q1, any value greater than that is treated as an outlier and processed.

Standardization is to solve the problem of scale inconsistency between different features. This study uses Z-score normalization, where all eigenvalues are converted to the same scale. Specifically, for feature C, its normalized value can be calculated by the following formula: Normalized value = (original value - mean)/standard deviation.

After the above steps, a clean and standardized feature selection dataset was obtained.

The goal of feature selection was to select the features most useful for breast cancer prediction from a list of 30 features. This study uses a tree-based model, such as a random forest, to assess the importance of features. A random forest model can output a significant score for each feature, based on how much the feature contributes to building the decision tree.

Suppose this study trains a random forest model that gives an importance score for each feature. The study could set a threshold, such as selecting features that scored above average in importance. If the importance score of 30 features is, then the average importance score is: average importance score = Σ(importance score of features)/number of features.

These characteristics are then selected for this study. For example, if the importance scores of features D, E, and F are 0.09, 0.15, and 0.07, respectively, and the average importance score is 0.08, then feature D and E are selected, but not feature F, because its score is below average.

Finally, this study obtained an optimized feature set consisting of 10 features, including tumor size, morphology, and nuclear features, which are of high importance in random forest models. Using this simplified feature set to train our machine learning models can improve the accuracy of predictions and reduce the risk of overfitting.

After completing feature selection, this study uses these selected features to construct the final predictive model. Support vector machine (SVM) or deep neural network algorithm was used to further train the model, and the model parameters were optimized by cross-validation and other methods to achieve effective prediction of breast cancer.

3.3. Machine learning model building

We chose Logistic Regression as the algorithm for building the model. Logistic regression is widely used in classification problems in the medical field, especially in disease prediction, because it can provide a probabilistic prediction of disease and the model is easy to interpret.

On the basis of data preprocessing and feature selection, it is assumed that This research have selected the following 10 key features: tumor size, morphology, density, nuclear characteristics, etc., and standardized them. This research take these characteristics as independent variables \( \(X\) \) and the occurrence of breast cancer (0 being negative and 1 being positive) as dependent variables \( \(Y\) \) .

The general form of logistic regression model can be expressed as:

\( \ [ P(Y=1) = \frac\lbrace 1\rbrace \lbrace 1+e\text{^}\lbrace -(\beta\_0 + \beta\_1 X\_1 + \beta\_2 X\_2 + ... + \beta\_n X\_n)\rbrace \rbrace \] \)

, \( \ (P (Y = 1) \] \) is a probability sample is positive, \( \ [e \] \) is a base of natural logarithms, \( \ \ beta\_0 \ is intercept, \ \ beta\_1 \ beta\_2,... , \beta\_n\) \) is the coefficient of each feature.

Table 1 is a simplified version of the sample dataset, showing the eigenvalues and their labels for five samples:

Table 1. Shows a simplified sample dataset

In this table, “Tumor Size”, “Shape”, “Density” and “Nuclear Feature” are the features obtained through data preprocessing and feature selection, and they have been standardized.

Next, This research used the 100 sample data to train the logistic regression model. In the training process, This research use the maximum likelihood estimation method to estimate the parameters \( \(\beta\_0, \beta\_1,... , \beta\_n\) \) . With iterative optimization algorithms, such as gradient descent, This research look for parameter values that maximize the likelihood function.

After training, This research have the coefficients \( \(\beta\_i\) \) for each feature, and the intercept term \( \(\beta\_0\) \) . For example, a trained model might look like this:

\( \ [P (Y = 1) = \ frac \lbrace 1\rbrace \lbrace 1 + e \text{^} \lbrace - (0.2 + 1.5 \ times X\_1-0.8 \ times X\_2 \ + 2.0 times X\_3 \ + 0.5 times X\_4 +...)\rbrace \rbrace \] \)

Where, 0.2 is the intercept term, 1.5, -0.8, 2.0, 0.5, etc. are the coefficients of corresponding features.

To evaluate the performance of the model, This research use metrics such as Confusion Matrix, Receiver Operating Characteristic Curve (ROC Curve), and AUC value (Area Under the ROC Curve). These indicators can fully reflect the accuracy, sensitivity and specificity of the model in the task of breast cancer prediction.

3.4. System implementation and testing

In the design and implementation of the breast cancer prediction system, the realization and testing of the system is an important step to verify the validity and feasibility of the model. This study selected 100 samples of medical data of breast cancer patients as experimental objects, which included clinical diagnosis information, patient personal information and biomarkers obtained through imaging. In the system implementation stage, This research first preprocessed these data, including missing value processing, outlier elimination and data standardization. After pretreatment, This research used feature selection technology to select the most predictive features from numerous biomarkers and clinical features, such as tumor size, tumor morphology, nuclear features, and edge features.

Next, This research built a machine learning model based on the selected features. Considering the characteristics of breast cancer data, This research chose support vector machine (SVM) as the main classification algorithm. SVM has good performance in binary classification, especially suitable for medical image data classification. This research use radial basis function (RBF) as the kernel function of SVM, and optimize the model parameters by Grid Search and Cross-Validation. In the process of parameter optimization, This research try different combinations of regularization parameter C and kernel function parameter γ, and finally determine a set of parameter values that can make the model achieve the best performance.

Specifically, the SVM model during training can be expressed in the following form:

Where is the input eigenvector, is the prediction result of the model, is the symbolic function,is the RBF kernel function, is the Lagrange multiplier, is the real label of the i th sample, is the biased term. The RBF kernel function is defined as:

Here, is the argument to the kernel function, which controls the width of the function.

After the model is trained, This research use an independent test set to evaluate the model’s predictive performance. The test set is composed of samples that have not participated in the training before, which ensures the fairness of the evaluation results. The performance of the model was measured by a series of indicators, including Accuracy, Sensitivity, Specificity, and AUC. Using the confusion matrix, This research can visually see how the model performs in predicting positive and negative breast cancer samples.

In the actual test, This research found that the accuracy of the model on the test set reached 85%, the sensitivity was 87%, the specificity was 82%, and the AUC value was 0.90, which indicates that the model has high prediction accuracy and good generalization ability. These results demonstrate that our breast cancer prediction system is effective and reliable in practical applications. However, This research also recognize that there is room for improvement in the model’s performance, such as by increasing the sample size, introducing more dimensional features, or experimenting with other advanced machine learning algorithms.

Overall, this study designed and implemented a machine learning-based breast cancer prediction system, and verified its effectiveness through experiments with 100 samples. Despite some limitations, this study provides valuable experience and reference for future machine learning applications in the field of breast cancer diagnosis.

4. Discussion

In this study, This research discuss the breast cancer prediction system based on machine learning in detail, including the design, implementation and testing of the system. In the discussion phase, This research focus on analyzing the applicability of machine learning models for breast cancer prediction, the reasons for model inefficiencies, and possible solutions.

First, the applicability analysis of machine learning models in breast cancer prediction shows that these models can effectively process large amounts of complex medical data and extract valuable information from it to aid diagnosis. By using different algorithms and techniques, such as support vector machines (SVM), random forests (RF), deep learning, etc., researchers have successfully developed a variety of prediction models that have achieved remarkable results in terms of accuracy and sensitivity. However, despite existing studies showing positive results, in practical applications, machine learning models still face challenges in data quality, model generalization ability, interpretability, and so on.

Secondly, the reasons for the insufficient model efficiency can be mainly attributed to the following points: First, the limitations of the data set. In many cases, the training data may be biased or the sample size is insufficient, which will directly affect the learning effect and generalization ability of the model. The second is the difficulty of feature selection. Correctly selecting features that have a significant impact on breast cancer prediction is a challenge because pathological features can vary widely from patient to patient. The third is the tradeoff between model complexity and interpretability. While complex models may provide higher accuracy, overly complex models can also reduce their interpretability, which is a problem that cannot be ignored in the medical field.

In order to solve the above problems, This research propose the following solutions: First, increase the diversity and size of the data set. By collecting broader and more comprehensive data, the generalization ability of the model can be improved. Second, advanced feature selection method is adopted. For example, statistics-based approaches, model-based approaches, or deep learning approaches can be used to identify and select features with the most predictive value. The third is to improve the interpretability of the model. Using interpretable machine learning frameworks or developing new interpretable tools can help doctors better understand the basis for model predictions.

In this study, This research not only demonstrate the design and implementation process of a machine learning-based breast cancer prediction system, but also explore the challenges and limitations of the model in practical application. Future research can work more deeply on improving data quality, optimizing feature selection, and improving model interpretability to promote the further development of machine learning in the field of breast cancer prediction.

5. Conclusion

5.1. Summarize the full text

In this study, This research deeply discuss the design and implementation of a machine learning-based breast cancer prediction system. Through a comprehensive literature review and theoretical analysis, This research determined that the application of machine learning in breast cancer detection is feasible and necessary. This research considered a variety of machine learning algorithms, including their strengths and limitations, and selected the algorithm that best suited the characteristics of the breast cancer data to build the predictive model.

In the process of system design and implementation, This research first established a clear system architecture, including data flow and processing flow. The data preprocessing and feature selection phases are the focus of our work because they directly affect the performance of subsequent models. This research used a range of methods to clean and standardize the data, and applied statistical and machine learning techniques to select features that would help in breast cancer prediction.

In the process of building the machine learning model, This research not only select the appropriate algorithm, but also carry out detailed training and parameter tuning to the model. This research ensure the performance of the model on the training set and avoid overfitting by cross-validation. In the system implementation and testing stage, This research choose the appropriate development environment and tools, and adopt scientific testing methods to evaluate the performance of the system. The test results show that the accuracy, sensitivity and specificity of our system have reached a high level, which proves the effectiveness of the system.

5.2. Proposed research limitations

Despite the positive results of our study, This research are aware of its limitations. For example, there is still room for improvement in the size and diversity of data sets, and further research is needed on feature selection and model interpretation. Our research sets the stage for future work in this area and points to possible directions for improvement.

5.3. Future research direction

Future research can be carried out from the following aspects: expanding the size and diversity of the dataset to enhance the generalization ability of the model; Explore more advanced feature selection methods to further improve the prediction accuracy; Improve the interpretability of the model so that doctors and patients can better understand the decision-making process of the model; And explore new methods such as ensemble learning to improve the robustness of the prediction system. In addition, with the continuous progress of artificial intelligence technology, the application of more innovative technologies to breast cancer prediction is also an important direction of future research.

Overall, this study provides valuable insights at both theoretical and practical levels, and provides clear guidance and recommendations for future research in the field of breast cancer prediction. This research believe that with the continuous development of technology and abundant data resources, the machine learning-based breast cancer prediction system will be more widely used and play an important role in improving the efficiency and accuracy of breast cancer diagnosis.

Acknowledgments

Authors wishing to acknowledge assistance or encouragement from colleagues, special work by technical staff or financial support from organizations should do so in an unnumbered Acknowledgments section immediately following the last numbered section of the paper.