A Naive Bayes Approach for Improving Heart Disease Detection on Healthcare Monitoring Through IoT and WSN
Keywords:
Machine Learning, Classification, Heart disease, WSN, IoTAbstract
Cardiovascular disease has become a prominent health concern among the medical community. This study proposes a unique approach to identify and forecast cardiac illness by using wireless sensor networks (WSN) and the Internet of Things (IoT). The suggested methodology employs the Naive Bayes algorithm for the analysis and categorization of health data. The timely identification of heart conditions enhances the probability of successful treatment and management under the guidance of a medical professional. Due to insufficient accuracy in classifying patient information, the existing healthcare monitoring system that relies on IoT devices and classification algorithms addresses a substantial challenge that could result in incorrect diagnoses and inappropriate treatment choices. The primary goal of this approach is to combine IoT with WSN to develop a real-time, dependable monitoring system that will enhance early identification and intervention for people at risk for heart disease. The proposed Naive Bayes classifier observes accuracy classes such as ROC, Recall, TP rate, F-measure, and FP rate. The obtained accuracy rate is compared with the existing approaches Backtracking Search-Based Deep Neural Network (BS-DNN) [17] and Convolutional Neural Network (CNN) [18]. The comparison result proves that the proposed Naïve Bayes has attained a classification accuracy of 97 %, far better than the others.
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