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Health is not everything, however, everything else is nothing without health. Then and now, people are trying out ways which can increase the longevity of life. Still, technology is far away from achieving this goal of reducing the mortality rate. However, for a start, the beginning steps have been done. Advancing technology and its influence on peoples' life is already leading to healthy lifestyles. Healthy living habits, routines, proactive health monitoring, and early detection of diseases lead to increased expectancy of life. Today, the world is adopting Internet of Things in its daily uses. There are various wearable technological devices that have been developed to monitor/measure different health attributes. But none of them processes the recorded data to provide future health assistance using real-time monitoring. This paper aims to develop an ML based model to detect heart diseases. In this case, KNN outstands as the best algorithm in comparison to other algorithms such as Random Forest, Decision Tree, Support Vector Machine and Naive Bayes. Furthermore, a prototype is developed to validate the results. The prototype consisted of a set of sensors to monitor the health of a person that causes heart diseases. It is finally predicted whether a person is prone to suffer from heart disease or not based on the model trained previously. Thus, our solution not only provides a significant human benefit but also enables proactive health monitoring data with a predicting accuracy of 88.52%.
Gupta et al. (Sun,) studied this question.