AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes

• Published in: The Journal of supercomputing Vol. 77; no. 5; pp. 5198 - 5219
• Main Authors: Jackins, V., Vimal, S., Kaliappan, M., Lee, Mi Young
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Artificial Intelligence for Smart Cities; Classification; Compilers; Computer Science; Datasets; Heart diseases; Interpreters; Processor Architectures; Programming Languages; Signs and symptoms; Diabetes; Random forest classification; Artificial intelligence; Naïve Bayes classification; Data mining techniques
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-020-03481-x

Healthcare practices include collecting all kinds of patient data which would help the doctor correctly diagnose the health condition of the patient. These data could be simple symptoms observed by the subject, initial diagnosis by a physician or a detailed test result from a laboratory. Thus, these data are 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 many disease datasets like diabetes, heart disease, and cancer 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 dataset used.