Disease Prediction Using a Modified Multi-Layer Perceptron Algorithm in Diabetes

Karan Dayal; Manmohan Shukla; Satyasundara Mahapatra

doi:10.4108/eetpht.9.3926

Authors

Karan Dayal Pranveer Singh Institute of Technology
Manmohan Shukla Pranveer Singh Institute of Technology
Satyasundara Mahapatra Pranveer Singh Institute of Technology

DOI:

https://doi.org/10.4108/eetpht.9.3926

Keywords:

MLP, SVM, ML, Diabetes, Prediction

Abstract

This paper presents an adaptation of the Multi-Layer Perceptron (MLP) algorithm for use in predicting diabetes risk. The aim is to enhance the accuracy and generalizability of the model by incorporating preprocessing techniques, dimensionality reduction using Principal Component Analysis (PCA), and improvements in optimization and regularization. Several factors, including glucose level, pregnancy, blood pressure, and body mass index, are taken into account when analyzing the PIMA Indian Diabetes dataset. Modern optimization methods, dropout regularization, and an adaptive learning rate are incorporated into the modified MLP model to fine-tune the model's weights and boost its predictive abilities. The effectiveness of the modified MLP algorithm is evaluated by comparing its performance with baseline machine learning methods and the original MLP algorithm in terms of accuracy, sensitivity, and specificity. The results of this study can improve the quality of healthcare provided to people at risk for developing diabetes and thus contribute to the development of better prediction models for the disease.

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References

Pallathadka, H., Mustafa, M., Sanchez, D. T., Sajja, G. S., Gour, S., & Naved, M. (2021). Impact of machine learning on management, healthcare and agriculture. Materials Today: Proceedings.

Khan, B., Naseem, R., Shah, M. A., Wakil, K., Khan, A., Uddin, M. I., & Mahmoud, M. (2021). Software defect prediction for healthcare big data: an empirical evaluation of machine learning techniques. Journal of Healthcare Engineering, 2021. DOI: https://doi.org/10.1155/2021/8899263

Fetaji, B., Fetaji, M., Ebibi, M., & Ali, M. (2021). Predicting Diabetes Using Diabetes Datasets and Machine Learning Algorithms: Comparison and Analysis. In Emerging Technologies in Computing: 4th EAI/IAER International Conference, iCETiC 2021, Virtual Event, August 18–19, 2021, Proceedings 4 (pp. 185-193). Springer International Publishing.Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. Jama, 319(13), 1317-1318. DOI: https://doi.org/10.1007/978-3-030-90016-8_13

Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195–197 (1981) DOI: https://doi.org/10.1016/0022-2836(81)90087-5

May, P., Ehrlich, H.C., Steinke, T.: ZIB Structure Prediction Pipeline: Composing a Complex Biological Workflow through Web Services. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 1148–1158. Springer, Heidelberg (2006) DOI: https://doi.org/10.1007/11823285_121

Ferdous, M., Debnath, J., & Chakraborty, N. R. (2020, July). Machine learning algorithms in healthcare: A literature survey. In 2020 11th International conference on computing, communication and networking technologies (ICCCNT) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/ICCCNT49239.2020.9225642

Lyngdoh A. C., Choudhury N. A. and Moulik. S, "Diabetes Disease Prediction Using Machine Learning Algorithms," 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Langkawi Island, Malaysia, 2021, pp. 517-521, doi: 10.1109/IECBES48179.2021.9398759. DOI: https://doi.org/10.1109/IECBES48179.2021.9398759

Ayad M., Kanaan H. and Ayache M., "Diabetes Disease Prediction Using Artificial Intelligence," 2020 21st International Arab Conference on Information Technology (ACIT), Giza, Egypt, 2020, pp. 1-6, doi: 10.1109/ACIT50332.2020.9300066. DOI: https://doi.org/10.1109/ACIT50332.2020.9300066

Shetty D., Rit K., Shaikh S. and Patil S., "Diabetes disease prediction using data mining," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, 2017, pp. 1-5, doi: 10.1109/ICIIECS.2017.8276012. DOI: https://doi.org/10.1109/ICIIECS.2017.8276012

Reshmi S., Biswas S. K., Boruah A. N., Thounaojam D. M. and Purkayastha B., "Diabetes Prediction Using Machine Learning Analytics," 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), Faridabad, India, 2022, pp. 108-112, doi: 10.1109/COM-IT-CON54601.2022.9850922. DOI: https://doi.org/10.1109/COM-IT-CON54601.2022.9850922

Dubey Y., Wankhede P., Borkar T., Borkar A. and Mitra K., "Diabetes Prediction and Classification using Machine Learning Algorithms," 2021 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), Dhaka, Bangladesh, 2021, pp. 60-63, doi: 10.1109/BECITHCON54710.2021.9893653. DOI: https://doi.org/10.1109/BECITHCON54710.2021.9893653

Swarna S. R., Boyapati S., Dixit P. and Agrawal R., "Diabetes prediction by using Big Data Tool and Machine Learning Approaches," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020, pp. 750-755, doi: 10.1109/ICISS49785.2020.9315866. DOI: https://doi.org/10.1109/ICISS49785.2020.9315866

Sarwar M. A., Kamal N., Hamid W. and Shah M. A., "Prediction of Diabetes Using Machine Learning Algorithms in Healthcare," 2018 24th International Conference on Automation and Computing (ICAC), Newcastle Upon Tyne, UK, 2018, pp. 1-6, doi: 10.23919/IConAC.2018.8748992. DOI: https://doi.org/10.23919/IConAC.2018.8748992

Emon M. U., Keya M. S., Kaiser, M. A. islam M. S., Tanha T. and Zulfiker M. S., "Primary Stage of Diabetes Prediction using Machine Learning Approaches," 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India, 2021, pp. 364-367, doi: 10.1109/ICAIS50930.2021.9395968. DOI: https://doi.org/10.1109/ICAIS50930.2021.9395968

Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213-237. DOI: https://doi.org/10.1016/j.ymssp.2018.05.050

Mienye, I. D. (2021). Improved Machine Learning Algorithms with Application to Medical Diagnosis. University of Johannesburg (South Africa).

https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database

Mijwil, M. M., & Abttan, R. A. (2021). Utilisation of machine learning techniques in testing and training of different medical datasets. Asian Journal of Computer and Information Systems (ISSN: 2321–5658), 9(4). DOI: https://doi.org/10.24203/ajcis.v9i4.6765

Scott, I., Carter, S., & Coiera, E. (2021). Clinician checklist for assessing suitability of machine learning applications in healthcare. BMJ Health & Care Informatics, 28(1).. DOI: https://doi.org/10.1136/bmjhci-2020-100251