Multi chronic disease prediction: A survey

Research Article
Open access

Multi chronic disease prediction: A survey

Chunduru Anilkumar 1* , Seepana Kanchana 2 , Sasapu Bharath Kumar 3 , Reddy Pravallika 4 , Surapureddi Mrudula 5
  • 1 Dept of Information Technology, GMR Institute of Technology, Rajam, Andhra Pradesh-532127    
  • 2 Dept of Information Technology, GMR Institute of Technology, Rajam, Andhra Pradesh-532127    
  • 3 Dept of Information Technology, GMR Institute of Technology, Rajam, Andhra Pradesh-532127    
  • 4 Dept of Information Technology, GMR Institute of Technology, Rajam, Andhra Pradesh-532127    
  • 5 Dept of Information Technology, GMR Institute of Technology, Rajam, Andhra Pradesh-532127    
  • *corresponding author anilkumar.ch@gmrit.edu.in
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

People today deal with a variety of illnesses as a result of their lifestyle choices and the environment. As a result, many people have chronic diseases that go untreated for long periods of time, imposing a tremendous impact on society. Therefore, predicting disease sooner is becoming a crucial duty. in order to systematically evaluate patients' future disease risks using their medical records. But for a doctor, making an accurate forecast based on symptoms is too challenging. The hardest task is making an accurate diagnosis of a condition. For this problem to be resolved, illness detection requires the use of deep learning and machine learning approaches. The amount of data in the medical sciences grows significantly every year. Earlier, health care for patient care has benefited from precise medical data analysis due of the development of information in the medical and healthcare areas. Prior identification and therapy are usually necessary to prevent chronic aeropathy from getting worse. Machine learning and deep learning algorithms are used to predict chronic diseases. Eight illness categories were our predictions. The Random Forest ensemble learning approach fared best overall. Finding the sickness prediction techniques with the highest accuracy and computation efficiency is the aim of this study.

Keywords:

Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Flask, Random Forest.

Anilkumar,C.;Kanchana,S.;Kumar,S.B.;Pravallika,R.;Mrudula,S. (2023). Multi chronic disease prediction: A survey. Applied and Computational Engineering,5,273-278.
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References

[1]. Dahiwade, Dhiraj, GajananPatle, and EktaaMeshram. "Designing disease prediction model using machine learning approach." In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 1211-1215. IEEE, 2019.

[2]. Yaganteeswarudu, Akkem. "Multi disease prediction model by using machine learning and Flask API." In 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 1242-1246. IEEE, 2020.

[3]. Singh, Y.K., Sinha, N. and Singh, S.K., 2016, November. Heart disease prediction system using random forest. In International Conference on Advances in Computing and Data Sciences (pp. 613-623). Springer, Singapore.

[4]. Devika, R., Sai VaishnaviAvilala, and V. Subramaniyaswamy. "Comparative study of classifier for chronic kidney disease prediction using naive bayes, KNN and random forest." 2019 3rd International conference on computing methodologies and communication (ICCMC). IEEE, 2019.

[5]. Khalilia, Mohammed, Sounak Chakraborty, and Mihail Popescu. "Predicting disease risks from highly imbalanced data using random forest." BMC medical informatics and decision making 11.1 (2011): 1-13.

[6]. Lyngdoh, Arwatki Chen, Nurul Amin Choudhury, and SoumenMoulik. "Diabetes Disease Prediction Using Machine Learning Algorithms." In 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 517-521. IEEE, 2021.

[7]. Ampavathi, Anusha, and T. VijayaSaradhi. "Multi disease-prediction framework using hybrid deep learning: an optimal prediction model." Computer Methods in Biomechanics and Biomedical Engineering 24.10 (2021): 1146-1168.

[8]. Kunjir, Ajinkya, HarshalSawant, and Nuzhat F. Shaikh. "Data mining and visualization for prediction of multiple diseases in healthcare." 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). IEEE, 2017.

[9]. Wang, Tingyan, Yuanxin Tian, and Robin G. Qiu. "Long short-term memory recurrent neural networks for multiple diseases risk prediction by leveraging longitudinal medical records." IEEE journal of biomedical and health informatics 24, no. 8 (2019): 2337-2346.

[10]. Rath, Adyasha, Debahuti Mishra, Ganapati Panda, and Suresh Chandra Satapathy. "Heart disease detection using deep learning methods from imbalanced ECG samples." Biomedical Signal Processing and Control 68 (2021): 102820.

[11]. Subramanian, M., Lv, N. P., & VE, S. (2022). Hyperparameter optimization for transfer learning of VGG16 for disease identification in corn leaves using Bayesian optimization. Big Data, 10(3), 215-229.

Cite this article

Anilkumar,C.;Kanchana,S.;Kumar,S.B.;Pravallika,R.;Mrudula,S. (2023). Multi chronic disease prediction: A survey. Applied and Computational Engineering,5,273-278.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Dahiwade, Dhiraj, GajananPatle, and EktaaMeshram. "Designing disease prediction model using machine learning approach." In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 1211-1215. IEEE, 2019.

[2]. Yaganteeswarudu, Akkem. "Multi disease prediction model by using machine learning and Flask API." In 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 1242-1246. IEEE, 2020.

[3]. Singh, Y.K., Sinha, N. and Singh, S.K., 2016, November. Heart disease prediction system using random forest. In International Conference on Advances in Computing and Data Sciences (pp. 613-623). Springer, Singapore.

[4]. Devika, R., Sai VaishnaviAvilala, and V. Subramaniyaswamy. "Comparative study of classifier for chronic kidney disease prediction using naive bayes, KNN and random forest." 2019 3rd International conference on computing methodologies and communication (ICCMC). IEEE, 2019.

[5]. Khalilia, Mohammed, Sounak Chakraborty, and Mihail Popescu. "Predicting disease risks from highly imbalanced data using random forest." BMC medical informatics and decision making 11.1 (2011): 1-13.

[6]. Lyngdoh, Arwatki Chen, Nurul Amin Choudhury, and SoumenMoulik. "Diabetes Disease Prediction Using Machine Learning Algorithms." In 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 517-521. IEEE, 2021.

[7]. Ampavathi, Anusha, and T. VijayaSaradhi. "Multi disease-prediction framework using hybrid deep learning: an optimal prediction model." Computer Methods in Biomechanics and Biomedical Engineering 24.10 (2021): 1146-1168.

[8]. Kunjir, Ajinkya, HarshalSawant, and Nuzhat F. Shaikh. "Data mining and visualization for prediction of multiple diseases in healthcare." 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). IEEE, 2017.

[9]. Wang, Tingyan, Yuanxin Tian, and Robin G. Qiu. "Long short-term memory recurrent neural networks for multiple diseases risk prediction by leveraging longitudinal medical records." IEEE journal of biomedical and health informatics 24, no. 8 (2019): 2337-2346.

[10]. Rath, Adyasha, Debahuti Mishra, Ganapati Panda, and Suresh Chandra Satapathy. "Heart disease detection using deep learning methods from imbalanced ECG samples." Biomedical Signal Processing and Control 68 (2021): 102820.

[11]. Subramanian, M., Lv, N. P., & VE, S. (2022). Hyperparameter optimization for transfer learning of VGG16 for disease identification in corn leaves using Bayesian optimization. Big Data, 10(3), 215-229.

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