The efficiency of hidden naïve bayes technique compared with data mining techniques in early diagnosis of diabetes and prediction system
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 p...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Universiti Malaysia Pahang
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/28848/1/34.%20The%20efficiency%20of%20hidden%20na%C3%AFve%20bayes%20technique.pdf http://umpir.ump.edu.my/id/eprint/28848/2/34.1%20The%20efficiency%20of%20hidden%20na%C3%AFve%20bayes%20technique.pdf |
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| author | Bassam Abdo, Al-Hameli Alsewari, Abdulrahman A. |
| author_facet | Bassam Abdo, Al-Hameli Alsewari, Abdulrahman A. |
| author_sort | Bassam Abdo, Al-Hameli |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-03-06T12:43:49Z |
| format | Conference or Workshop Item |
| id | UMPir28848 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T12:43:49Z |
| publishDate | 2020 |
| publisher | Universiti Malaysia Pahang |
| record_format | dspace |
| spelling | UMPir288482022-06-20T06:01:46Z http://umpir.ump.edu.my/id/eprint/28848/ The efficiency of hidden naïve bayes technique compared with data mining techniques in early diagnosis of diabetes and prediction system Bassam Abdo, Al-Hameli Alsewari, Abdulrahman A. QA76 Computer software 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. Universiti Malaysia Pahang 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28848/1/34.%20The%20efficiency%20of%20hidden%20na%C3%AFve%20bayes%20technique.pdf pdf en http://umpir.ump.edu.my/id/eprint/28848/2/34.1%20The%20efficiency%20of%20hidden%20na%C3%AFve%20bayes%20technique.pdf 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) (Unpublished) |
| spellingShingle | QA76 Computer software Bassam Abdo, Al-Hameli Alsewari, Abdulrahman A. The efficiency of hidden naïve bayes technique compared with data mining techniques in early diagnosis of diabetes and prediction system |
| title | The efficiency of hidden naïve bayes technique compared with data mining techniques in early diagnosis of diabetes and prediction system |
| title_full | The efficiency of hidden naïve bayes technique compared with data mining techniques in early diagnosis of diabetes and prediction system |
| title_fullStr | The efficiency of hidden naïve bayes technique compared with data mining techniques in early diagnosis of diabetes and prediction system |
| title_full_unstemmed | The efficiency of hidden naïve bayes technique compared with data mining techniques in early diagnosis of diabetes and prediction system |
| title_short | The efficiency of hidden naïve bayes technique compared with data mining techniques in early diagnosis of diabetes and prediction system |
| title_sort | efficiency of hidden naive bayes technique compared with data mining techniques in early diagnosis of diabetes and prediction system |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/28848/1/34.%20The%20efficiency%20of%20hidden%20na%C3%AFve%20bayes%20technique.pdf http://umpir.ump.edu.my/id/eprint/28848/2/34.1%20The%20efficiency%20of%20hidden%20na%C3%AFve%20bayes%20technique.pdf |
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