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Diabetes Prediction and Management Using Machine Learning Approaches.

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Název: Diabetes Prediction and Management Using Machine Learning Approaches.
Alternate Title: Predicción y Manejo de la Diabetes Usando Enfoques de Aprendizaje Automático. (Spanish)
Autoři: Alzboon, Mowafaq Salem, Alqaraleh, Muhyeeddin, Al-Batah, Mohammad Subhi
Zdroj: Data & Metadata; 2025, Vol. 4, p1-15, 15p
Témata: DIABETES, MACHINE learning, ALGORITHMS, HEALTH risk assessment, CONTROL groups, WELL-being, FORECASTING
Abstract (English): Diabetes has emerged as a significant global health issue, especially with the increasing number of cases in many countries. This trend Underlines the need for a greater emphasis on early detection and proactive management to avert or mitigate the severe health complications of this disease. Over recent years, machine learning algorithms have shown promising potential in predicting diabetes risk and are beneficial for practitioners. Objective: This study highlights the prediction capabilities of statistical and non-statistical machine learning methods over Diabetes risk classification in 768 samples from the Pima Indians Diabetes Database. It consists of the significant demographic and clinical features of age, body mass index (BMI) and blood glucose levels that greatly depend on the vulnerability against Diabetes. The experimentation assesses the various types of machine learning algorithms in terms of accuracy and effectiveness regarding diabetes prediction. These algorithms include Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting and Neural Network Models. The results show that the Neural Network algorithm gained the highest predictive accuracy with 78,57%, and then the Random Forest algorithm had the second position with 76,30% accuracy. These findings show that machine learning techniques are not just highly effective. Still, they also can potentially act as early screening tools in predicting Diabetes within a data-driven fashion with valuable information on who is more likely to get affected. In addition, this study can help to realize the potential of machine learning for timely intervention over the longer term, which is a step towards reducing health outcomes and disease burden attributable to Diabetes on healthcare systems. [ABSTRACT FROM AUTHOR]
Abstract (Spanish): La diabetes se ha convertido en un problema de salud global significativo, especialmente debido al aumento en el número de casos en muchos países. Esta tendencia subraya la necesidad de dar mayor énfasis a la detección temprana y la gestión proactiva para evitar o mitigar las graves complicaciones de salud asociadas con esta enfermedad. En los últimos años, los algoritmos de aprendizaje automático han demostrado un potencial prometedor para predecir el riesgo de diabetes y son beneficiosos para los profesionales de la salud. Objetivo: Este estudio resalta las capacidades predictivas de los métodos estadísticos y no estadísticos de aprendizaje automático en la clasificación del riesgo de diabetes en 768 muestras de la base de datos Pima Indians Diabetes. Incluye características demográficas y clínicas significativas, como de masa corporal (IMC) y los niveles de glucosa en sangre, que están estrechamente relacionados con la vulnerabilidad frente a la diabetes. La experimentación evalúa varios tipos de algoritmos de aprendizaje automático en términos de precisión y efectividad para la predicción de la diabetes. Estos algoritmos incluyen Regresión Logística, Árboles de Decisión, Bosques Aleatorios, Vecinos Más Cercanos (KNN), Naive Bayes, Máquinas de Soporte Vectorial (SVM), Gradient Boosting y Modelos de Redes Neuronales. Los resultados muestran que el algoritmo de Redes Neuronales obtuvo la mayor precisión predictiva con un 78,57 %, seguido por el algoritmo de Bosques Aleatorios con un 76,30 % de precisión. Estos hallazgos demuestran que las técnicas de aprendizaje automático no solo son altamente efectivas, sino que también tienen el potencial de actuar como herramientas de detección temprana para predecir la diabetes de manera basada en datos, proporcionando información valiosa sobre quién tiene mayor probabilidad de verse afectado. Además, este estudio puede contribuir a comprender el potencial del aprendizaje automático para intervenciones oportunas a largo plazo, lo que representa un paso hacia la reducción de los resultados negativos de salud y la carga de la enfermedad atribuible a la diabetes en los sistemas de atención médica. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
Popis
Abstrakt:Diabetes has emerged as a significant global health issue, especially with the increasing number of cases in many countries. This trend Underlines the need for a greater emphasis on early detection and proactive management to avert or mitigate the severe health complications of this disease. Over recent years, machine learning algorithms have shown promising potential in predicting diabetes risk and are beneficial for practitioners. Objective: This study highlights the prediction capabilities of statistical and non-statistical machine learning methods over Diabetes risk classification in 768 samples from the Pima Indians Diabetes Database. It consists of the significant demographic and clinical features of age, body mass index (BMI) and blood glucose levels that greatly depend on the vulnerability against Diabetes. The experimentation assesses the various types of machine learning algorithms in terms of accuracy and effectiveness regarding diabetes prediction. These algorithms include Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting and Neural Network Models. The results show that the Neural Network algorithm gained the highest predictive accuracy with 78,57%, and then the Random Forest algorithm had the second position with 76,30% accuracy. These findings show that machine learning techniques are not just highly effective. Still, they also can potentially act as early screening tools in predicting Diabetes within a data-driven fashion with valuable information on who is more likely to get affected. In addition, this study can help to realize the potential of machine learning for timely intervention over the longer term, which is a step towards reducing health outcomes and disease burden attributable to Diabetes on healthcare systems. [ABSTRACT FROM AUTHOR]
ISSN:29534917
DOI:10.56294/dm2025545