Comparative Analysis on Machine Learning Algorithms for Multiple Disease Prediction
Keywords:
K-Nearest Neighbor (KNN), ), Random Forest, Decision Tree, Naïve Bayes, Disease Prediction, Machine LearningAbstract
These days, majority of the humans are suffered from multiple diseases because of eating habits and environmental situations. Hence, predication of these multiple diseases become a challenging and critical task in these days. Machine Learning (ML) algorithms becomes more popular to predict multiple diseases. For the multiple disease prediction, in this paper, we investigated and examined various ML algorithms such as Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN) used for accurate prediction of disease. For analysis of the ML-based classification algorithms, this paper intently used Accuracy as a performance metric and tested on the DiseaseSymptomKB dataset. The accuracy of general disease prediction by using Decision Tree is 95%, Random Forest is 95%, Naïve Bayes is 95% and KNN is 92%.
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