Comparative analysis on liver benchmark datasets and prediction using supervised learning techniques
Year: 2024
Type: article
Abstract: Disease diagnosis is most challenging task today. Different datasets are available in web source that contains important features to diagnose the diseases. This paper explores different classification algorithms on medical liver bench mark datasets like BUPA and Indian Liver patient dataset (ILPD). The ILPD is best fit for the model and also gives high classifier accuracy. In proposed model the following classifiers like Naïve Bayes (NB), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) classification, multi-layer perceptron (MLP), artificial neural network (ANN), deep belief network (DBN) and probabilistic neural network (PNN) are used. The results shown that ILPD is best dataset for all classifiers and RF classification in particular is best classifier.
Authors: Tilakachuri Balakrishna, Jagadeeswara Rao Annam, Dasari Haritha
Institution: Jawaharlal Nehru Technological University, Kakinada
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4402039193