An MLP-CNN Model for Real-time Health Monitoring and Intervention
Received: 30 April 2024 | Revised: 18 May 2024 | Accepted: 26 May 2024 | Online: 14 June 2024
Corresponding author: C. Atheeq
Abstract
The use of Artificial Intelligence (AI) in healthcare, particularly in real-time health monitoring and predictive interventions for chronic diseases, has many benefits but also many drawbacks. Existing health risk prediction algorithms face accuracy issues and, due to the wide variety of health profiles, general algorithm applicability is problematic. The proposed model solves this issue by using an advanced AI framework to improve the accuracy of the prediction of Chronic Kidney Disease (CKD) and eliminate false positives. Our hybrid Deep Learning (DL) method blends a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN), thus including real-time feedback to help the system learn from its predictions and results. The proposed system solves a major problem in this area and sets a new benchmark for AI applications in healthcare by directly addressing prediction accuracy. It offers more tailored, accurate, and responsive chronic disease management, improving patient outcomes and healthcare resource efficiency.
Keywords:
Artificial Intelligence, Healthcare, Predictive Interventions, Adaptive Learning Systems, Chronic Disease, Deep Learning, Chronic Diseases, CKDDownloads
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Copyright (c) 2024 Mohammad Khaja Nizamuddin, Syed Raziuddin, Mohammed Farheen, C. Atheeq, Ruhiat Sultana
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