Work

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

Year: 2020

Type: article

Abstract: 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.

Source: The Journal of Supercomputing

DOI: 10.1007/s11227-020-03481-x

Authors: V. Jackins, S. Vimal, M. Kaliappan, Mi Young Lee

Institutions: National Engineering College, Sejong University

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,750