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Title |
Diabetes Detection and Forecasting using Machine Learning Approaches: Current State-of-the-art |
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Author |
Alwalid Alhashem, Aiman Abdulbaset, Faisal Almudarra, Hazzaa Alshareef, Mshari Alqasoumi, Atta-ur Rahman, and Maqsood Mahmud |
| Citation |
Vol. 23 No. 10 pp. 199-208 |
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Abstract |
The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field. |
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Keywords |
AI, Machine Learning, Deep Learning, Diabetes detection. Prediction |
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