Diseases Prediction Model using Machine Learning Technique

Abstract

Now a day, people face various diseases due to the environmental condition and living habits of them. So prediction of disease at earlier stage becomes important task. But the prediction on the basis of symptoms becomes too difficult for doctor. The correctly prediction of disease is most challenging task. To overcome this problem data mining plays an important and efficient way to predict the disease. Medical science has huge amount of data growth per year. Due to increase amount of data growth in medical and healthcare field the accurate analysis on medical data which has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in the large amount of medical data. We have designed the heart disease prediction system. We proposed multiple disease prediction based on symptoms of the patient. For the heart disease prediction, we used knn , naïve bayes machine learning algorithm for accurate prediction of disease. For disease prediction required disease symptoms dataset. Here we focused on heart disease prediction, because the heart disease is one of the leading causes of death among all other diseases. The heart disease prediction contains that whether the patient suffer from heart disease or not by using naïve bayes and KNN algorithm. In this heart disease prediction, the living habits of person and checkup information consider for the accurate prediction. The accuracy of heart disease prediction by using naïve bayes is 94.5% which is more than KNN algorithm. And the time and the memory requirement is also more in KNN than naïve bayes. After heart disease prediction, this system able to gives the risk associated with heart disease which is lower risk of heart disease or higher. For the risk prediction, we are using CNN algorithm.