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 (Journal of Pharmaceutical Negative Results, 2022)
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.