Hybrid Deep Learning and Supervised Machine Learning Approaches for Accurate Diabetes Diagnosis from Electronic Health Records
Keywords:
Diabetes Diagnosis, Hybrid Machine Learning, Long Short-Term Memory (LSTM), Random Forest, Electronic Health Records (EHR), Explainable AI, SHAPAbstract
Accurate and early diagnosis of diabetes mellitus is crucial to prevent severe complications andoptimize treatment strategies. Electronic Health Records (EHR) contain rich longitudinal clinicaldata that can be leveraged to develop predictive models for diabetes diagnosis. This paper proposesa hybrid approach combining
References
D. Kavakiotis, O. Tsave, A. Salifoglou, et al., "Machine Learning and Data Mining Methods in Diabetes Research," Comput. Struct. Biotechnol. J., vol. 15, pp. 104–116, 2017.
N. Alizadehsani, T. Roshanzamir, S. Hussain, et al., "Risk Prediction of Diabetes: Big Data Mining with Fusion of Genetic and Clinical Information," Comput. Biol. Med., vol. 87, pp. 96–109, 2017.
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Published
2024-05-20
How to Cite
Dr. Brajesh Kumar. (2024). Hybrid Deep Learning and Supervised Machine Learning Approaches for Accurate Diabetes Diagnosis from Electronic Health Records. Journal of Computational Analysis and Applications (JoCAAA), 33(05), 2488–2500. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3462
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