The efficiency of hidden naïve bayes technique compared with data mining techniques in early diagnosis of diabetes and prediction system

Bassam Abdo, Al-Hameli and Alsewari, Abdulrahman A. (2020) The efficiency of hidden naïve bayes technique compared with data mining techniques in early diagnosis of diabetes and prediction system. In: 1st International Conference of Advanced Computing and Informatics (ICACIn 2020) , 13 - 15 April 2020 , Casablanca, Morocco. pp. 1-15.. (Unpublished)

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Abstract

The efficiency of classification techniques largely varies on the techniques used and the data sets. A classifier process efficiency lies in how accurately, it categorizes the item. The technique of classification finds the relationships between the predictor's worth and the goal values. This paper is an in-depth study of the Hidden Naïve Bayes (HNB) classification technique compared to state-of-the-art techniques in the medical field, which have demonstrated HNB efficiency and ability to increase the accuracy of prediction. This study examines the efficiency of the four machine learning techniques including HNB, Decision Tree C4.5, Naive Bayes (NB), and Support Vector Machine (SVM) on the diabetes data set to identify the possibility of creating predictive models with real impact. The four classification techniques are studied and analyzed, then their efficiency is evaluated for the PID dataset in terms of accuracy, precision, F-measure, and recall, in addition to other performance measures. The outcome of this analysis shows that HNB is more reliable than other techniques.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Classification; Hiden Naïve Bayes; Naïve Bayes;Decision Tree; Support Vactor Machine; Data Mining; Pima Indian Diabetes Dataset.
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computing
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 20 Jun 2022 06:01
Last Modified: 20 Jun 2022 06:01
URI: http://umpir.ump.edu.my/id/eprint/28848
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