Secure Heart Disease Classification System Based on Three Pass Protocol and Machine Learning

Abstract

Heart disease is one of the worst life-threatening conditions. Correct and early diagnosis of this disease is crucial for saving patients’ life and avoiding other complications. On the other hand, keeping the patient’s data, diagnosis process, and treatment plan secured is equally important to the defactomedical procedure. This research proposes a system that is consisting of two phases: security provision and patients’ condition diagnosis. Typically, the first phase exercises a security protocol, called three-pass protocol, to ensure that the people who can access the patient's information are authorized. In order to obtain a high accuracy level in the diagnosis process, artificial intelligence with machine learning methods are employed in the later phase. The proposed system relies on a data set which includes a number of vital indicators, by which the patient's status can be classified as having heart disease or not. The KNN algorithm and the random forest tree algorithm are applied to carry out the classification task. The accuracy scale results reveals that the randomforest tree algorithm (99%) gave higher accuracy than KNN (97%).

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“…Figure 2 illustrates the organizers of this conference and a proposal document for the Dartmouth summer research project on artificial intelligence. Artificial intelligence is a complete description of all computers capable of performing tasks that require human intelligence in processing information, extracting outcomes and features, making decisions, and solving issues [41][42][43]. Recent years have witnessed a vast and significant development in artificial intelligence technologies and a large public demand for the use of these techniques, especially in the domain of healthcare, education, and many others.…”