Machine Learning for Health Insurance Prediction in Nigeria
Health insurance coverage remains critical to healthcare accessibility, particularly in developing nations like Nigeria. This paper focused on predicting the likelihood of medical insurance coverage among individuals in Nigeria by employing four prominent Machine learning techniques: Logistic Regre...
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| Format: | Article |
| Language: | English |
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College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria
2024-12-01
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| Series: | ABUAD Journal of Engineering Research and Development |
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| Online Access: | https://www.journals.abuad.edu.ng/index.php/ajerd/article/view/648 |
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| author | Victor Enemona Ochigbo Oluwasogo Adekunle Okunade Emmanuel Gbenga Dada Oluyemi Mikail Olaniyi Oluwatoyosi Victoria Oyewande |
| author_facet | Victor Enemona Ochigbo Oluwasogo Adekunle Okunade Emmanuel Gbenga Dada Oluyemi Mikail Olaniyi Oluwatoyosi Victoria Oyewande |
| author_sort | Victor Enemona Ochigbo |
| collection | DOAJ |
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Health insurance coverage remains critical to healthcare accessibility, particularly in developing nations like Nigeria. This paper focused on predicting the likelihood of medical insurance coverage among individuals in Nigeria by employing four prominent Machine learning techniques: Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine classifiers. The dataset utilized for analysis comprises demographic information, socioeconomic factors, and health-related variables collected from a diverse sample across Nigeria. Four models are trained and evaluated: Logistic Regression widely accepted for its simplicity and interpretability. Random Forest is a robust ensemble learning algorithm capable of capturing complex relationships within the data. The decision Tree model is simple to understand and visualize and the Support Vector Machine model is known for producing a very good classification. Furthermore, the performance metrics uutilized to rate the predictive capabilities of the models are Accuracy, Precision, Sensitivity, F Score, and area under the Receiver Operating Characteristic (AUC & ROC Curve). Additionally, a features importance analysis is conducted for the identification of the dominant factors contributing to the prediction of the spread of medical insurance in Nigeria. The outcome of this paper gives insights in the efficiency of each machine learning models used to forecast medical insurance coverage, and identifying key determinants influencing insurance coverage can assist policymakers and healthcare stakeholders in devising targeted strategies to improve healthcare access and affordability for the Nigerian people.
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| format | Article |
| id | doaj-art-e73e654d5e9c4634b008177b0daa2209 |
| institution | OA Journals |
| issn | 2756-6811 2645-2685 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria |
| record_format | Article |
| series | ABUAD Journal of Engineering Research and Development |
| spelling | doaj-art-e73e654d5e9c4634b008177b0daa22092025-08-20T02:01:04ZengCollege of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, NigeriaABUAD Journal of Engineering Research and Development2756-68112645-26852024-12-017210.53982/ajerd.2024.0702.52-jMachine Learning for Health Insurance Prediction in NigeriaVictor Enemona Ochigbo0Oluwasogo Adekunle Okunade1Emmanuel Gbenga Dada2Oluyemi Mikail Olaniyi3Oluwatoyosi Victoria Oyewande4Department of Knowledge Management and Communication, Agricultural Research Council of Nigeria, Abuja, NigeriaDepartment of Computer Science, Faculty of Sciences, National Open University of Nigeria, Abuja, NigeriaDepartment of Computer Science, Faculty of Physical Sciences, University of Maiduguri, Maiduguri, NigeriaDepartment of Computer Science, Faculty of Sciences, National Open University of Nigeria, Abuja, NigeriaDepartment of Computer Science, Faculty of Sciences, National Open University of Nigeria, Abuja, Nigeria Health insurance coverage remains critical to healthcare accessibility, particularly in developing nations like Nigeria. This paper focused on predicting the likelihood of medical insurance coverage among individuals in Nigeria by employing four prominent Machine learning techniques: Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine classifiers. The dataset utilized for analysis comprises demographic information, socioeconomic factors, and health-related variables collected from a diverse sample across Nigeria. Four models are trained and evaluated: Logistic Regression widely accepted for its simplicity and interpretability. Random Forest is a robust ensemble learning algorithm capable of capturing complex relationships within the data. The decision Tree model is simple to understand and visualize and the Support Vector Machine model is known for producing a very good classification. Furthermore, the performance metrics uutilized to rate the predictive capabilities of the models are Accuracy, Precision, Sensitivity, F Score, and area under the Receiver Operating Characteristic (AUC & ROC Curve). Additionally, a features importance analysis is conducted for the identification of the dominant factors contributing to the prediction of the spread of medical insurance in Nigeria. The outcome of this paper gives insights in the efficiency of each machine learning models used to forecast medical insurance coverage, and identifying key determinants influencing insurance coverage can assist policymakers and healthcare stakeholders in devising targeted strategies to improve healthcare access and affordability for the Nigerian people. https://www.journals.abuad.edu.ng/index.php/ajerd/article/view/648Ensemble Technique Odd RatioConfusion MatrixFeature ImportanceMedical Insurance |
| spellingShingle | Victor Enemona Ochigbo Oluwasogo Adekunle Okunade Emmanuel Gbenga Dada Oluyemi Mikail Olaniyi Oluwatoyosi Victoria Oyewande Machine Learning for Health Insurance Prediction in Nigeria ABUAD Journal of Engineering Research and Development Ensemble Technique Odd Ratio Confusion Matrix Feature Importance Medical Insurance |
| title | Machine Learning for Health Insurance Prediction in Nigeria |
| title_full | Machine Learning for Health Insurance Prediction in Nigeria |
| title_fullStr | Machine Learning for Health Insurance Prediction in Nigeria |
| title_full_unstemmed | Machine Learning for Health Insurance Prediction in Nigeria |
| title_short | Machine Learning for Health Insurance Prediction in Nigeria |
| title_sort | machine learning for health insurance prediction in nigeria |
| topic | Ensemble Technique Odd Ratio Confusion Matrix Feature Importance Medical Insurance |
| url | https://www.journals.abuad.edu.ng/index.php/ajerd/article/view/648 |
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