An Accurate Heart Disease Prognosis Using Machine Intelligence and IoMT

In recent years, Internet of Medical Things (IoMT) and machine learning (ML) have played a major role in the healthcare industry and prediction of in time diagnosis of diseases. Heart disease has long been considered one of the most common and lethal causes of death. Accordingly, in this paper, a multiple-step method using IoMT and ML has been proposed for diagnosis of heart disease based on image and numerical resources. In the first step, transfer learning based on convolutional neural network (CNN) is used for feature extraction. In the second step, three methods of distributed stochastic neighbor embedding (t-SNE), F-score, and correlation-based feature selection (CFS) are utilized to select the best features. In the end, a combination of outputs of three classifiers including Gaussian Bayes (GB), support vector machine (SVM), and random forest (RF) according to the majority voting is employed for diagnosis of the conditions of heart disease patients. The results were evaluated on the two UCI datasets. The results indicate the improvement of performance compared to other methods.