Disease Prediction Using Machine Learning Algorithms KNN and CNN

Nowadays, people face various diseases due to environmental conditions and their living habits. So the prediction of disease at an earlier stage becomes an important task. But the accurate prediction based on symptoms becomes too difficult for the doctor. The correct prediction of disease is the most challenging task. To overcome this problem data mining plays an important role to predict the disease. Medical science has a large amount of data growth per year. Due to the increasing amount of data growth in the medical and healthcare field the accurate analysis of medical data has been the benefit of early patient care. With the help of disease data, data mining finds hidden pattern information in a huge amount of medical data. We proposed general disease prediction based on the symptoms of the patient. For disease prediction, we use K-Nearest Neighbor (KNN) and Convolutional neural network (CNN) machine learning algorithms for the accurate prediction of disease. Disease prediction required a disease symptoms dataset. In this general disease prediction, the living habits of a person and checkup information consider for the accurate prediction. The accuracy of general disease prediction by using CNN is 84.5% which is more than the KNN algorithm. And the time and the memory requirement are also more in KNN than in CNN. After general disease prediction, this system can give the risk associated with the general disease which is a lower risk of general disease or higher.