Prediction of Clinical Disease with AI-Based Multiclass Classification Using Naïve Bayes and Random Forest Classifier

• Published in: Advances in Artificial Intelligence and Applied Cognitive Computing pp. 841 - 849
• Main Authors: Jackins, V., Vimal, S., Kaliappan, M., Lee, Mi Young
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2021
• Series: Transactions on Computational Science and Computational Intelligence
• Subjects: Artificial Intelligence; Data mining techniques; Diabetes; Naïve Bayes classification; Random Forest classification
• ISBN: 9783030702953; 3030702952
• ISSN: 2569-7072; 2569-7080
• DOI: 10.1007/978-3-030-70296-0_63

Healthcare practices include collecting all kinds of patient data which would help the doctor correctly diagnose the health condition of the patient. This data could be simple symptoms observed by the subject, initial diagnosis by a physician or a detailed test result from a lab. Thus, far this data is only utilized for analysis by a doctor who then ascertains the disease using his/her personal medical expertise. The Artificial Intelligence has been used with Naive Bayes classification and Random Forest classification algorithm to classify disease datasets of heart disease, to check whether the patient is affected by that disease or not. A performance analysis of the disease data for both algorithms is calculated and compared. The results of the simulations show the effectiveness of the classification techniques on a dataset, as well as the nature and complexity of the data set used.