A Novel Approach for Prediction of Human Disease using Symptoms by Multilayer Perceptron Algorithm to Improve the Accuracy and Compared with Gaussian Naïve Bayes Algorithm
DOI:
https://doi.org/10.47750/pnr.2022.13.S04.099Keywords:
Novel Multilayer Perceptron, Naive Bayes, Machine Learning, Symptoms, Disease, Decision.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 naive Bayes algorithm .
Materials and Methods : Novel 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, it shows that there is a statistically insignificant difference between the two study groups with value p=0.212 (p> 0.05). It was observed that the novel multilayer perceptron algorithm obtains an accuracy of 95%. It appears to have better accuracy than the naive Bayes algorithm (92%).
Conclusion: The results prove that novel multilayer perceptron algorithm approaches with varied seed value have significant improvement in disease prediction using symptoms.
