Open AccessPredictive Tool for Dermatology Disease Diagnosis using Machine Learning Techniques
Author(s) -
M. Sudha,
B. Poorva
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.g5376.078919
Subject(s) - naive bayes classifier , random forest , support vector machine , bayes' theorem , disease , artificial intelligence , machine learning , computer science , pattern recognition (psychology) , medicine , bayesian probability , pathology
Prediction of skin diseases is more complex as many diseases have the same symptoms at the early stage but may vary at the later stages while the disease becomes incurable. So we can use data mining algorithms to classify the diseases based on the input symptoms. In this paper, the best algorithm suitable for classification of data into six dermatological diseases is determined by comparison with few other algorithms. Naive Bayes tends to show higher accuracy of 99.31% , Random forest exhibits 97.80% and SVM reveals 94.35% when test size is 40% in jupyter notebook. Linear regression and K Nearest Neighbors when trained with 80% of the data displays 82.14% and 94.44% accuracy respectively. Naive Bayes can be used for the prediction of several other diseases and is best for classification of data and thus helps doctors predict the disease more accurately and with comparatively lesser time.