Effective Diabetes Prediction using an IoT-based Integrated Ensemble Machine Learning Framework
Received: 31 August 2024 | Revised: 5 October 2024, 27 October 2024, and 3 November 2024 | Accepted: 25 December 2024 | Online: 2 February 2025
Corresponding author: Rashi Rastogi
Abstract
Diabetes, a prevalent chronic disease, affects a significant global population. Identifying and being aware of key variables promptly may substantially enhance results for both patients and public health efforts. Systematic methods such as monitoring diabetic patients allow the collection of extensive data from diabetic patients. When it comes to keeping track of a patient's health, IoT sensors, such as those used in diabetic patient monitoring systems, are invaluable. Blood glucose levels, body temperature, and location of a diabetic patient can be tracked and recorded through a monitoring device. In addition to monitoring patients, these data can be classified using Machine Learning (ML) methods. This study applies three ML models to three different diabetes datasets and analyzes their performance. According to the results, the fine-tuned random forest model achieved higher accuracy, i.e., 89%, 90%, and 99%.
Keywords:
diabetes prediction, machine learning, diabetes patent monitoring, IoT, chronic illnessDownloads
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Copyright (c) 2024 Rashi Rastogi, Mamta Bansal, Naveen Kumar, Sanjay Singla, Priti Singla, Ram Avtar Jaswal
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