Enhancing Diabetes Management: A Hybrid Adaptive Machine Learning Approach for Intelligent Patient Monitoring in e-Health Systems

被引:0
作者
Dohare, Sushil [1 ]
Deeba, K. [2 ]
Pamulaparthy, Laxmi [3 ]
Abdufattokhov, Shokhjakhon [4 ,5 ]
Ramesh, Janjhyam Venkata Naga [6 ]
El-Ebiary, Yousef A. Baker [7 ]
Thenmozhi, E. [8 ]
机构
[1] Jazan Univ, Coll Publ Hlth & Trop Med, Dept Epidemiol, Jazan, Saudi Arabia
[2] REVA Univ, Sch Comp Sci & Applicat, Bangalore, India
[3] Vignana Bharathi Inst Technol, JNTUH Autonomous, Hyderabad, India
[4] Turin Polytech Univ Tashkent, Automat Control & Comp Engn Dept, Tashkent, Uzbekistan
[5] Tashkent Int Univ Educ, Dept Informat Technol, Tashkent, Uzbekistan
[6] Koneru Lakshmaiah Educ Fdn, Dept Comp Sci & Engn, Vaddeswaram 522302, Andhra Pradesh, India
[7] UniSZA Univ, Fac Informat & Comp, Kuala Terengganu, Malaysia
[8] Panimalar Engn Coll, Dept Informat Technol, Chennai, India
关键词
Diabetes; machine learning; convolutional neural network; support vector machine; grey wolf optimization; e-health systems;
D O I
10.14569/IJACSA.2024.0150162
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The goal of the present research is to better understand the need of accurate and ongoing monitoring in the complicated chronic metabolic disease known as diabetes. With the integration of an intelligent system utilising a hybrid adaptive machine learning classifier, the suggested method presents a novel way to tracking individuals with diabetes. The system uses cutting edge technologies like intelligent tracking and machine learning (ML) to improve the efficacy and accuracy of diabetes patient monitoring. Integrating smart gadgets, sensors, and telephones in key locations to gather full body dimension data that is essential for diabetic health forms the architectural basis. Using a dataset that includes comprehensive data on the patient's characteristics and glucose levels, this investigation looks at sixty-two diabetic patients who were followed up on a daily basis for sixty-seven days. The study presents a hybrid architecture that combines a Convolutional Neural Network (CNN) with a Support Vector Machine (SVM) in order to optimise system performance. To train and optimise the hybrid model, Grey Wolf Optimisation (GWO) is utilised, drawing inspiration from collaborative optimisation in wolf packs. Thorough assessment, utilising standardised performance criteria including recall, F1-Score, accuracy, precision, and the Receiver Operating Characteristic (ROC) Curve, methodically verifies the suggested solution. The results reveal a remarkable 99.6% accuracy rate, which shows a considerable increase throughout training epochs. The CNN-SVM hybrid model achieves a classification accuracy advantage of around 4.15% over traditional techniques such as SVM, Decision Trees, and Sequential Minimal Optimisation. Python software is used to implement the suggested CNN-SVM technique. This research advances e-health systems by presenting a novel framework for effective diabetic patient monitoring that integrates machine learning, intelligent tracking, and optimisation techniques. The results point to a great deal of promise for the proposed method in the field of medicine, especially in the accurate diagnosis and follow-up of diabetic patients, which would provide opportunities for tailored and adaptable patient care.
引用
收藏
页码:631 / 644
页数:14
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