Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well?
The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
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MDPI AG, Basel, Switzerland
2022
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| Online Access: | https://eprints.ums.edu.my/id/eprint/32758/1/Hypertension%20prediction%20in%20adolescents%20using%20anthropometric%20measurements%2C%20Do%20machine%20learning%20models%20perform%20equally%20well.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/32758/2/Hypertension%20Prediction%20in%20Adolescents%20Using%20Anthropometric%20Measurements%2C%20Do%20Machine%20Learning%20Models%20Perform%20Equally%20Well.pdf |
| _version_ | 1825714751696535552 |
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| author | Chai, Soo See Goh, Kok Luong Cheah, Whye Lian Chang, Yee Hui Robin Ng, Giap Weng |
| author_facet | Chai, Soo See Goh, Kok Luong Cheah, Whye Lian Chang, Yee Hui Robin Ng, Giap Weng |
| author_sort | Chai, Soo See |
| collection | UMS |
| description | The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. It is essential to assess the impacts of the chosen machine learning models using simple anthropometric measurements. We developed and tested 13 machine learning methods of neural network, ensemble, and classical categories to predict hypertension in adolescents using only simple anthropometric measurements. The imbalanced dataset of 2461 samples with 30.1% hypertension subjects was first partitioned into 90% for training and 10% for validation. The training dataset was reduced to eight simple anthropometric measurements: age, C index, ethnicity, gender, height, location, parental hypertension, and waist circumference using correlation coefficient. The Synthetic Minority Oversampling Technique (SMOTE) combined with random under-sampling was used to balance the dataset. The models with optimal hyperparameters were assessed using accuracy, precision, sensitivity, specificity, F1-score, misclassification rate, and AUC on the testing dataset. Across all seven performance measures, no model consistently outperformed the others. LightGBM was the best model for all six performance metrics, except sensitivity, whereas Decision Tree was the worst. We proposed using Bayes’ Theorem to assess the models’ applicability in the Sarawak adolescent population, resulting in the top four models being LightGBM, Random Forest, XGBoost, and CatBoost, and the bottom four models being Logistic Regression, LogitBoost, SVM, and Decision Tree. This study demonstrates that the choice of machine learning models has an effect on the prediction outcomes. |
| first_indexed | 2024-03-06T03:16:28Z |
| format | Article |
| id | ums.eprints-32758 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:16:28Z |
| publishDate | 2022 |
| publisher | MDPI AG, Basel, Switzerland |
| record_format | dspace |
| spelling | ums.eprints-327582022-06-09T04:14:02Z https://eprints.ums.edu.my/id/eprint/32758/ Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well? Chai, Soo See Goh, Kok Luong Cheah, Whye Lian Chang, Yee Hui Robin Ng, Giap Weng QA76.75-76.765 Computer software RC31-1245 Internal medicine The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. It is essential to assess the impacts of the chosen machine learning models using simple anthropometric measurements. We developed and tested 13 machine learning methods of neural network, ensemble, and classical categories to predict hypertension in adolescents using only simple anthropometric measurements. The imbalanced dataset of 2461 samples with 30.1% hypertension subjects was first partitioned into 90% for training and 10% for validation. The training dataset was reduced to eight simple anthropometric measurements: age, C index, ethnicity, gender, height, location, parental hypertension, and waist circumference using correlation coefficient. The Synthetic Minority Oversampling Technique (SMOTE) combined with random under-sampling was used to balance the dataset. The models with optimal hyperparameters were assessed using accuracy, precision, sensitivity, specificity, F1-score, misclassification rate, and AUC on the testing dataset. Across all seven performance measures, no model consistently outperformed the others. LightGBM was the best model for all six performance metrics, except sensitivity, whereas Decision Tree was the worst. We proposed using Bayes’ Theorem to assess the models’ applicability in the Sarawak adolescent population, resulting in the top four models being LightGBM, Random Forest, XGBoost, and CatBoost, and the bottom four models being Logistic Regression, LogitBoost, SVM, and Decision Tree. This study demonstrates that the choice of machine learning models has an effect on the prediction outcomes. MDPI AG, Basel, Switzerland 2022-01-02 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/32758/1/Hypertension%20prediction%20in%20adolescents%20using%20anthropometric%20measurements%2C%20Do%20machine%20learning%20models%20perform%20equally%20well.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/32758/2/Hypertension%20Prediction%20in%20Adolescents%20Using%20Anthropometric%20Measurements%2C%20Do%20Machine%20Learning%20Models%20Perform%20Equally%20Well.pdf Chai, Soo See and Goh, Kok Luong and Cheah, Whye Lian and Chang, Yee Hui Robin and Ng, Giap Weng (2022) Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well? Applied Sciences, 12 (3). pp. 1-17. ISSN 2076-3417 https://www.mdpi.com/2076-3417/12/3/1600 https://doi.org/10.3390/app12031600 https://doi.org/10.3390/app12031600 |
| spellingShingle | QA76.75-76.765 Computer software RC31-1245 Internal medicine Chai, Soo See Goh, Kok Luong Cheah, Whye Lian Chang, Yee Hui Robin Ng, Giap Weng Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well? |
| title | Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well? |
| title_full | Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well? |
| title_fullStr | Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well? |
| title_full_unstemmed | Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well? |
| title_short | Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well? |
| title_sort | hypertension prediction in adolescents using anthropometric measurements do machine learning models perform equally well |
| topic | QA76.75-76.765 Computer software RC31-1245 Internal medicine |
| url | https://eprints.ums.edu.my/id/eprint/32758/1/Hypertension%20prediction%20in%20adolescents%20using%20anthropometric%20measurements%2C%20Do%20machine%20learning%20models%20perform%20equally%20well.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/32758/2/Hypertension%20Prediction%20in%20Adolescents%20Using%20Anthropometric%20Measurements%2C%20Do%20Machine%20Learning%20Models%20Perform%20Equally%20Well.pdf |
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