Chronic Disease Prediction Using Deep Learning

Nowadays data is growing rapidly in bioscience and health protection, in clinical information, an exact investigation can benefit early infection identification, patients’ social insurance, and community services. Prediction is an significant aspect in the health care domain. In this paper, we establish ML and deep learning algorithms for Prediction of patients’ chronic diseases. Experiment with the refitted prediction model from the standard dataset available. Objective of this paper is to forecast chronic diseases in the individual patient by using the machine learning method, K-nearest neighbor, decision tree and deep learning using (RELU or Rectified linear activation function, sigmoid activation function, deep sequential network) and Adam as an optimizer. Examine to several ordinary algorithms, the accuracy of the proposed system is enhanced. With the comparison of other algorithms, deep learning algorithms will give better accuracy it’s about 98.3%. These techniques are applied to predict heart, breast cancer, and diabetes chronic diseases.