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Volume 21, Issue 3
  • ISSN: 1573-3998
  • E-ISSN: 1875-6417

Abstract

Background

Diabetes Mellitus is a chronic health condition (long-lasting) due to inadequate control of blood levels of glucose. This study presents a prediction of Type 2 Diabetes Mellitus among women using various Machine Learning Algorithms deployed to predict the diabetic condition. A University of California Irvine Diabetes Mellitus Dataset posted in Kaggle was used for analysis.

Methods

The dataset included eight risk factors for Type 2 Diabetes Mellitus prediction, including Age, Systolic Blood Pressure, Glucose, Body Mass Index, Insulin, Skin Thickness, Diabetic Pedigree Function, and Pregnancy. R language was used for the data visualization, while the algorithms considered for the study are Logistic Regression, Support Vector Machines, Decision Trees and Extreme Gradient Boost. The performance analysis of these algorithms on various classification metrics is also presented here, considering the Area Under the Curve and Receiver Operating Characteristics score is the best for Extreme Gradient Boost with 85%, followed by Support Vector Machines and Decision Trees.

Results

The Logistic Regression is showing low performance. But the Decision Trees and Extreme Gradient Boost show promising performance against all the classification metrics. But the Support Vector Machines offers a lower support value; hence it cannot be claimed to be a good classifier. The model showed that the most significant predictors of Type 2 Diabetes Mellitus were strongly correlated with Glucose Levels and mediumly correlated with Body Mass Index, whereas Age, Skin Thickness, Systolic Blood Pressure, Insulin, Pregnancy, and Pedigree Function were less significant. This type of real-time analysis has proved that the symptoms of Type 2 Diabetes Mellitus in women fall entirely different compared to men, which highlights the importance of Glucose Levels and Body Mass Index in women.

Conclusion

The prediction of Type 2 Diabetes Mellitus helps public health professionals to help people by suggesting proper food intake and adjusting lifestyle activities with good fitness management in women to make glucose levels and body mass index controlled. Therefore, the healthcare systems should give special attention to diabetic conditions in women to reduce exacerbations of the disease and other associated symptoms. This work attempts to predict the occurrence of Type 2 Diabetes Mellitus among women on their behavioral and biological conditions.

© 2025 Bentham Science Publishers
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2024-06-20
2024-11-22

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Using Machine Learning and Artificial Intelligence to Predict Diabetes Mellitus among Women Population
Curr. Diabetes Rev. 21, E050623217669 (2025); https://doi.org/10.2174/1573399820666230605160212
/content/journals/cdr/10.2174/1573399820666230605160212
/content/journals/cdr/10.2174/1573399820666230605160212
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  • Article Type:     Research Article
Keyword(s): body mass index (BMI); decision tree; diabetes mellitus; Digital health; Logistic Regression (LR); prediction model

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