Role of Machine Learning Concepts in Disease Prediction using Patient’s Health History

Abstract

Medical service industry has aimed out to be a large market. The human services industry creates a lot of medicinal services knowledge from time to time that can utilize to retrieve data for predicting illness that can happen to a patient. In the future old disease history, data of a particular patient may be useful for the immediate cause of disease. Additionally, this territory needs improvement by using the enlightening information in medicinal services. To handle the enormous patient information to get hidden symptoms of diseases with regular programming not support well. Machine learning techniques are well suited to extract, analyze, predict and visualization of unknown data. In this paper, we consider the patient's data from the New York-Presbyterian Hospital. We trained the machine learning models, decision Tree, Naïve Bayes, Support Vector Machines to predict the kind of disease caused based on the symptoms. The approach used here implemented with the accuracy of 95.12% on the dataset of 150 diseases.