A Novel Approach for Prediction of Human Disease using Symptoms by Multilayer Perceptron Algorithm to Improve Accuracy and Compared with K Nearest Neighbor Algorithm

Abstract

Aim : The aim of this paper is to improve Accuracy in Disease prediction using symptoms by a novel multilayer perceptron
classifier in comparison with the K nearest neighbor algorithm .
Materials and Methods : Multilayer perceptron classifier and naive bayes algorithm sample size (N=10) to predict the accuracy percentage of predicted disease. G-power is calculated for two different groups, alpha (0.05), power (80%).
Results: Based on the measurement of data, statistical analysis, and independent sample T-test,there is a statistically insignificant difference between the two study groups with value p=0.768 (p>0.05) . It was observed that the novel Multilayer perceptron algorithm obtains the accuracy as 95%. It appears to have better accuracy than the K nearest neighbor (81%).
Conclusion: The results prove that the novel Multilayer perceptron algorithm approaches with varying seed value have significant
improvement in disease prediction using symptoms.