Cyber-Physical System (CPS) Based Heart Disease's Prediction Model for Community Clinic Using Machine Learning Classifiers
There are 13000 community clinics in Bangladesh to resolve rural people's health problems. The people in the rural area of our country have been affected by various common diseases, and these diseases are not properly diagnosed due to a lack of modern technologies. In this context, the risk of uncertainty in health management and treatment is not at a controllable level. Modern technology like Cyber-Physical systems (CPS) based health care can be considered an effective data collection, processing, and prediction tool for rural medical infrastructure to resolve the complexity of different diseases. In this paper, a CPS-based health care architecture is proposed. In addition, a methodology is suggested to process the real-time data for further taking the strategical decision in a very special way using different machine learning algorithms. A heart disease-related case study is considered to understand the proposed method clearly in practical application. Because of this, a heart disease dataset is collected from Kaggle resources. Using this dataset, different machine learning classifiers are used to develop a heart disease prediction model. The different classifier models associated with Random Forest (RF), K-Nearest Neighbors (K-NN), Naive Bayes (NB), Adaptive Boosting (AdB), Decision Tree Classifier (DTC), and Binomial Logistic Regression (BLR) are used. The results obtained from the prediction model are in good agreement with the experimental results, and accuracy is about 87% for the classifiers DTC. In comparing other available state-of-art models, the proposed model exhibits better efficiency in predicting future decision-making. Among the classifiers, DTC shows 87% accuracy for predicting heart disease. Using this Model diagnosis will be faster, correct, and help patients predict heart disease. When compared to other state-of-the-art models, our model outperforms them.
Keywords: heart disease, cyber-physical system, community clinics, classifier algorithm, decision trees.
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