Abstract
Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).
Highlights
It is difficult to identify heart disease because of several contributory risk factors such as diabetes, high blood pressure, high cholesterol, abnormal pulse rate and many other factors
The severity of the disease is classified based on various methods like K -Nearest Neighbor Algorithm (KNN), Decision Trees (DT), Genetic algorithm (GA), and Naive Bayes (NB) [11], [13]
The highest accuracy is achieved by hybrid random forest with a linear model (HRFLM) classification method in comparison with existing methods
Summary
It is difficult to identify heart disease because of several contributory risk factors such as diabetes, high blood pressure, high cholesterol, abnormal pulse rate and many other factors. Various techniques in data mining and neural networks have been employed to find out the severity of heart disease among humans. We generate results using a Artificial Neural Network ANN, which produces good performance in the prediction of heart disease [6], [18]. Neural network methods are introduced, which combine posterior probabilities and predicted values from multiple predecessor techniques This model achieves an accuracy level of up to 89.01% which is a strong results compared to previous works. Meidan et al collected and labeled network traffic data from nine distinct IoT devices, PCs and smartphones Using supervised learning, they trained a multi-stage meta classifier. The experiment results show that our proposed hybrid method has stronger capability to predict heart disease compared to existing methods.
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