A Novel Approach for Prediction of Human Disease using Symptoms by Multilayer Perceptron Algorithm to Improve Accuracy and Compared with Random Forest 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 Random Forest algorithm . Materials and Methods : Novel multilayer perceptron classifier and random forest 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=0.488 (p> 0.05). It was observed that the novel multilayer perceptron algorithm obtains accuracy as 95%. It appears to have better accuracy than the random forest (81%). Conclusion: The results prove that the novel multilayer perceptron algorithm there is a significant improvement in techniques with varied seed values in disease prediction using symptoms.