Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features
Diabetes is a global health concern that affects people of all races. With different uncertainties in human lifestyles, it is difficult to predict diabetes while assuming that the risk patterns are the same for all. The likelihood of diabetes in a patient is mostly predicted using machine learning (...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2024-01-01
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| Series: | Computing Open |
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| Online Access: | https://www.worldscientific.com/doi/10.1142/S2972370124500065 |
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| author | Ayodeji O. J. Ibitoye Joseph D. Akinyemi Olufade F. W. Onifade |
| author_facet | Ayodeji O. J. Ibitoye Joseph D. Akinyemi Olufade F. W. Onifade |
| author_sort | Ayodeji O. J. Ibitoye |
| collection | DOAJ |
| description | Diabetes is a global health concern that affects people of all races. With different uncertainties in human lifestyles, it is difficult to predict diabetes while assuming that the risk patterns are the same for all. The likelihood of diabetes in a patient is mostly predicted using machine learning (ML) models on features explicitly available in datasets, while the intrinsic relationship between features viz-a-viz their potential relevance to the presence of diabetes is oftentimes neglected. In this work, we explored feature importance and correlation to derive the top 15 feature pairs from a dataset of 263,882 samples of anonymized patient information. These top-15 feature pairs were fed into five different ML models (decision tree (DT), neural networks (NN), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB)) for predicting the likelihood of diabetes, while also feeding the direct features (without correlated pairing) separately into the same 5[Formula: see text]ML models. The models’ performances were evaluated using accuracy, precision, recall and F1-score and NN presented the best performance overall achieving an F1-score of 85% for the correlated feature pairs (CF) and 75% for the direct feature pairs. The results confirm the importance of the correlation/relationship between features in predicting the likelihood of diabetes in patients more accurately. |
| format | Article |
| id | doaj-art-b05f60f7b5334a0f947fbcd4ad046b38 |
| institution | Kabale University |
| issn | 2972-3701 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Computing Open |
| spelling | doaj-art-b05f60f7b5334a0f947fbcd4ad046b382025-02-04T03:24:11ZengWorld Scientific PublishingComputing Open2972-37012024-01-010210.1142/S2972370124500065Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral FeaturesAyodeji O. J. Ibitoye0Joseph D. Akinyemi1Olufade F. W. Onifade2School of Computing and Mathematical Sciences, University of Greenwich, SE10 9LS, London, United KingdomDepartment of Computer Science, University of York, YO10 5DD, York, United KingdomDepartment of Computer Science, University of Ibadan, NigeriaDiabetes is a global health concern that affects people of all races. With different uncertainties in human lifestyles, it is difficult to predict diabetes while assuming that the risk patterns are the same for all. The likelihood of diabetes in a patient is mostly predicted using machine learning (ML) models on features explicitly available in datasets, while the intrinsic relationship between features viz-a-viz their potential relevance to the presence of diabetes is oftentimes neglected. In this work, we explored feature importance and correlation to derive the top 15 feature pairs from a dataset of 263,882 samples of anonymized patient information. These top-15 feature pairs were fed into five different ML models (decision tree (DT), neural networks (NN), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB)) for predicting the likelihood of diabetes, while also feeding the direct features (without correlated pairing) separately into the same 5[Formula: see text]ML models. The models’ performances were evaluated using accuracy, precision, recall and F1-score and NN presented the best performance overall achieving an F1-score of 85% for the correlated feature pairs (CF) and 75% for the direct feature pairs. The results confirm the importance of the correlation/relationship between features in predicting the likelihood of diabetes in patients more accurately.https://www.worldscientific.com/doi/10.1142/S2972370124500065Diabetesmachine learningrisk predictionpaired relationshipdecision support |
| spellingShingle | Ayodeji O. J. Ibitoye Joseph D. Akinyemi Olufade F. W. Onifade Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features Computing Open Diabetes machine learning risk prediction paired relationship decision support |
| title | Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features |
| title_full | Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features |
| title_fullStr | Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features |
| title_full_unstemmed | Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features |
| title_short | Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features |
| title_sort | machine learning based diabetes risk prediction using associated behavioral features |
| topic | Diabetes machine learning risk prediction paired relationship decision support |
| url | https://www.worldscientific.com/doi/10.1142/S2972370124500065 |
| work_keys_str_mv | AT ayodejiojibitoye machinelearningbaseddiabetesriskpredictionusingassociatedbehavioralfeatures AT josephdakinyemi machinelearningbaseddiabetesriskpredictionusingassociatedbehavioralfeatures AT olufadefwonifade machinelearningbaseddiabetesriskpredictionusingassociatedbehavioralfeatures |