Intelligent Health Risk and Disease Prediction Using Optimized Naive Bayes Classifier

Abstract

Machine learning is the subset of Artificial Intelligence and it is used for prediction various real time data analytics applications. Health care monitoring is the major area to analyse the result and make effective decisions. We need intelligent and automated process for predicting diseases using medical dataset. Machine learning methods are proposed to handle the dataset. Smart healthcare prediction is proposed to identify the user or patient information or symptoms as an input. Our system has forecasting accuracy index based on likelihood of the disease and health information. We use Naive bayes classifier algorithm for handling classification, prediction and accuracy index of dataset. Our algorithm measures the disease percentage and train the dataset. Once the prediction result will appears based on effective decision to be taken. In our work, we are taken 20000 train dataset and 7500 test data set for evaluation. TensorFlow simulator is used to simulate the system and measure accuracy. In this system achieves 95% accuracy and performance result compared with existing methods.