Prediction of Diabetes Using Hidden Naïve Bayes: Comparative Study

Al-Hameli, Bassam Abdo and Alsewari, Abdulrahman A. and Alsarem, Mohammed (2021) Prediction of Diabetes Using Hidden Naïve Bayes: Comparative Study. In: Advances on Smart and Soft Computing: Proceedings of ICACIn 2020 , 13-14 April 2020 , Faculty of Sciences Ain Chock Casablanca, Hassan II University, Morocco. pp. 223-233., 1188. ISBN 978-981-15-6048-4

Pdf Prediction of Diabetes Using Hidden Naïve Bayes Comparative Study.pdf Restricted to Repository staff only Download (24MB) | Request a copy

Abstract

Classification techniques performance varies widely with the techniques and the datasets employed. A process performance classifier lies in how accurately it categorizes the item. The technique of classification finds the relationships between the value of the predictor and the values of the goal. This paper is an in-depth analysis study of the classification of algorithms in data mining field for the hidden Naïve Bayes (HNB) classifier compared to state-of-the-art medical classifiers which have demonstrated HNB performance and the ability to increase prediction accuracy. This study examines the overall performance of the four machine learning techniques strategies on the diabetes dataset, including HNB, decision tree (DT) C4.5, Naive Bayes (NB), and support vector machine (SVM), to identify the possibility of creating predictive models with real impact. The classification techniques are studied and analyzed; thus, their effectiveness is tested for the Pima Indian Diabetes dataset in terms of accuracy, precision, F-measure, and recall, besides other performance measures.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Advances in Intelligent Systems and Computing
Uncontrolled Keywords:	Classification Hidden Naïve Bayes Naïve Bayes Decision tree Support vector machine Data mining Pima Indian Diabetes dataset
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division:	Institute of Postgraduate Studies
Depositing User:	Noorul Farina Arifin
Date Deposited:	24 Nov 2021 08:55
Last Modified:	24 Nov 2021 08:55
URI:	http://umpir.ump.edu.my/id/eprint/32647
Download Statistic:	View Download Statistics

Actions (login required)

View Item