Prediction of Diabetes Using Hidden Naïve Bayes: Comparative Study
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. T...
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| Format: | Conference or Workshop Item |
| Language: | English |
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Springer
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/32647/ http://umpir.ump.edu.my/id/eprint/32647/1/Prediction%20of%20Diabetes%20Using%20Hidden%20Na%C3%AFve%20Bayes%20Comparative%20Study.pdf |
| _version_ | 1848824075012538368 |
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| author | Al-Hameli, Bassam Abdo Alsewari, Abdulrahman A. Alsarem, Mohammed |
| author_facet | Al-Hameli, Bassam Abdo Alsewari, Abdulrahman A. Alsarem, Mohammed |
| author_sort | Al-Hameli, Bassam Abdo |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:07:15Z |
| format | Conference or Workshop Item |
| id | ump-32647 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:07:15Z |
| publishDate | 2021 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-326472021-11-24T08:55:10Z http://umpir.ump.edu.my/id/eprint/32647/ Prediction of Diabetes Using Hidden Naïve Bayes: Comparative Study Al-Hameli, Bassam Abdo Alsewari, Abdulrahman A. Alsarem, Mohammed QA75 Electronic computers. Computer science 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. Springer 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/32647/1/Prediction%20of%20Diabetes%20Using%20Hidden%20Na%C3%AFve%20Bayes%20Comparative%20Study.pdf 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 (Published) https://doi.org/10.1007/978-981-15-6048-4_20 |
| spellingShingle | QA75 Electronic computers. Computer science Al-Hameli, Bassam Abdo Alsewari, Abdulrahman A. Alsarem, Mohammed Prediction of Diabetes Using Hidden Naïve Bayes: Comparative Study |
| title | Prediction of Diabetes Using Hidden Naïve Bayes: Comparative Study |
| title_full | Prediction of Diabetes Using Hidden Naïve Bayes: Comparative Study |
| title_fullStr | Prediction of Diabetes Using Hidden Naïve Bayes: Comparative Study |
| title_full_unstemmed | Prediction of Diabetes Using Hidden Naïve Bayes: Comparative Study |
| title_short | Prediction of Diabetes Using Hidden Naïve Bayes: Comparative Study |
| title_sort | prediction of diabetes using hidden naïve bayes: comparative study |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/32647/ http://umpir.ump.edu.my/id/eprint/32647/ http://umpir.ump.edu.my/id/eprint/32647/1/Prediction%20of%20Diabetes%20Using%20Hidden%20Na%C3%AFve%20Bayes%20Comparative%20Study.pdf |