Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?
Abstract Background The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle – income countries call for preventive public health interventions. Studies from Africa including those from Ghana, consistently reveal high T2DM-related mortality rates. While previous research in t...
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BMC
2025-01-01
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| Series: | BMC Endocrine Disorders |
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| Online Access: | https://doi.org/10.1186/s12902-025-01831-5 |
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| author | Godsway Edem Kpene Sylvester Yao Lokpo Sandra A. Darfour-Oduro |
| author_facet | Godsway Edem Kpene Sylvester Yao Lokpo Sandra A. Darfour-Oduro |
| author_sort | Godsway Edem Kpene |
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| description | Abstract Background The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle – income countries call for preventive public health interventions. Studies from Africa including those from Ghana, consistently reveal high T2DM-related mortality rates. While previous research in the Ho municipality has primarily examined risk factors, comorbidity, and quality of life of T2DM patients, this study specifically investigated mortality predictors among these patients. Method The study was retrospective involving medical records of T2DM patients. Data extracted included mortality outcome (dead or alive), sociodemographic characteristics (age, sex, marital status, educational level, occupation and location), family history of diseases (diabetes, cardiovascular disease (CVD), or asthma), lifestyle (smoking and alcohol intake), comorbidities (such as skin infections, sickle cell disease, urinary tract infections, and pneumonia) and complications of diabetes (CVD, nephropathy, neuropathy, foot ulcers, and diabetic ketoacidosis) were analyzed using Stata version 16.0 and Python 3.6.1 programming language. Both descriptive and inferential statistics were done to describe and build predictive models respectively. The performance of machine learning (ML) techniques such as support vector machine (SVM), decision tree, k nearest neighbor (kNN), eXtreme Gradient Boosting (XGBoost) and logistic regression were evaluated using the best-fitting predictive model for T2DM mortality. Results Of the 328 participants, 183 (55.79%) were female, and the percentage of mortality was 11.28%. A 100% mortality was recorded among the T2DM patients with sepsis (p-value = 0.012). T2DM in-patients were 3.83 times as likely to die [AOR = 3.83; 95% CI: (1.53–9.61)] if they had nephropathy compared to T2DM in-patients without nephropathy (p-value = 0.004). The full model which included sociodemographic characteristics, family history, lifestyle variables and complications of T2DM had the best prediction of T2DM mortality outcome (ROC = 72.97%). The accuracy for (test and train datasets) were as follows: (90% and 90%), (100% and 100%), (90% and 90%), (90% and 88%) and (88% and 90%) respectively for the various ML classification techniques: logistic regression, Decision tree classifier, kNN classifier, SVM and XGBoost. Conclusion This study found that all in-patients with sepsis died. Nephropathy was the identified significant predictor of T2DM mortality. Decision tree classifier provided the best classifying potential. |
| format | Article |
| id | doaj-art-6be1d7b4a3514932b6badf4b27ad5987 |
| institution | Kabale University |
| issn | 1472-6823 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Endocrine Disorders |
| spelling | doaj-art-6be1d7b4a3514932b6badf4b27ad59872025-01-12T12:27:07ZengBMCBMC Endocrine Disorders1472-68232025-01-0125111210.1186/s12902-025-01831-5Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?Godsway Edem Kpene0Sylvester Yao Lokpo1Sandra A. Darfour-Oduro2Department of Medical Laboratory Sciences, School of Allied Health Sciences, University of Health and Allied SciencesDepartment of Medical Laboratory Sciences, School of Allied Health Sciences, University of Health and Allied SciencesDepartment of Public Health Studies, Elon UniversityAbstract Background The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle – income countries call for preventive public health interventions. Studies from Africa including those from Ghana, consistently reveal high T2DM-related mortality rates. While previous research in the Ho municipality has primarily examined risk factors, comorbidity, and quality of life of T2DM patients, this study specifically investigated mortality predictors among these patients. Method The study was retrospective involving medical records of T2DM patients. Data extracted included mortality outcome (dead or alive), sociodemographic characteristics (age, sex, marital status, educational level, occupation and location), family history of diseases (diabetes, cardiovascular disease (CVD), or asthma), lifestyle (smoking and alcohol intake), comorbidities (such as skin infections, sickle cell disease, urinary tract infections, and pneumonia) and complications of diabetes (CVD, nephropathy, neuropathy, foot ulcers, and diabetic ketoacidosis) were analyzed using Stata version 16.0 and Python 3.6.1 programming language. Both descriptive and inferential statistics were done to describe and build predictive models respectively. The performance of machine learning (ML) techniques such as support vector machine (SVM), decision tree, k nearest neighbor (kNN), eXtreme Gradient Boosting (XGBoost) and logistic regression were evaluated using the best-fitting predictive model for T2DM mortality. Results Of the 328 participants, 183 (55.79%) were female, and the percentage of mortality was 11.28%. A 100% mortality was recorded among the T2DM patients with sepsis (p-value = 0.012). T2DM in-patients were 3.83 times as likely to die [AOR = 3.83; 95% CI: (1.53–9.61)] if they had nephropathy compared to T2DM in-patients without nephropathy (p-value = 0.004). The full model which included sociodemographic characteristics, family history, lifestyle variables and complications of T2DM had the best prediction of T2DM mortality outcome (ROC = 72.97%). The accuracy for (test and train datasets) were as follows: (90% and 90%), (100% and 100%), (90% and 90%), (90% and 88%) and (88% and 90%) respectively for the various ML classification techniques: logistic regression, Decision tree classifier, kNN classifier, SVM and XGBoost. Conclusion This study found that all in-patients with sepsis died. Nephropathy was the identified significant predictor of T2DM mortality. Decision tree classifier provided the best classifying potential.https://doi.org/10.1186/s12902-025-01831-5Type 2 diabetes MellitusPredictorsMortalityMachine learning techniquesModel |
| spellingShingle | Godsway Edem Kpene Sylvester Yao Lokpo Sandra A. Darfour-Oduro Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform? BMC Endocrine Disorders Type 2 diabetes Mellitus Predictors Mortality Machine learning techniques Model |
| title | Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform? |
| title_full | Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform? |
| title_fullStr | Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform? |
| title_full_unstemmed | Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform? |
| title_short | Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform? |
| title_sort | predictive models and determinants of mortality among t2dm patients in a tertiary hospital in ghana how do machine learning techniques perform |
| topic | Type 2 diabetes Mellitus Predictors Mortality Machine learning techniques Model |
| url | https://doi.org/10.1186/s12902-025-01831-5 |
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