Machine Learning and Smart Devices for Prediction of Heart Disease, Diabetes, and Obesity: Systematic Review
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 11 |
| Year of Publication : 2024 |
| Authors : Kirti Gupta, Pardeep Kumar, Shuchita Upadhyaya, Shalini Aggarwal |
10.14445/23488379/IJEEE-V11I11P122
How to Cite?
Kirti Gupta, Pardeep Kumar, Shuchita Upadhyaya, Shalini Aggarwal, "Machine Learning and Smart Devices for Prediction of Heart Disease, Diabetes, and Obesity: Systematic Review," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 11, pp. 222-230, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I11P122
Abstract:
Heart disease, diabetes, and obesity are major global health issues and are increasing day by day. There is a need for effective predictive strategies. Machine learning is vital for early detection and diagnosis. It provides timely and individualized healthcare interventions. This analysis evaluates optimal ML algorithms for heart disease, diabetes, and obesity. It identifies key risk factors and highlights the role of AI in healthcare. The study follows PRISMA guidelines. A broad search using Google Scholar and PubMed identified 30 studies from 2014 to 2024. The article explores the symptoms and aftereffects of diabetes, obesity, and heart disease. It reviews effective ML methods for forecasting these issues. AI-based methods help medical professionals diagnose diseases. Research shows that AI tools improve diagnosis speed and accuracy. This leads to more personalized treatment and better outcomes.
Keywords:
Diabetes, Healthcare, Heart disease, Machine Learning (ML), Obesity.
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