International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 01 | Jan 2025
p-ISSN: 2395-0072
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A Machine Learning Approach to Diabetes Prediction Hemangi Patil1, Harshal Patil2 Gaurav Acharya3, Roshan Verma4 1Independent Researcher, Mumbai, India 2Independent Researcher, Mumbai, India 3Department of master’s in computer science, IIT Chicago, Illinois
4Department of master’s in information technology management, IIT Chicago, Illinois
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Abstract - Diabetes is a growing global health issue, and
early detection can prevent its severe effects. This paper presents a machine learning-based approach to predict whether a person has diabetes or not using clinical data such as Glucose levels, Insulin, BMI, and Age. The Pima Indians Diabetes dataset is used for training and testing the model. A Support Vector Classifier (SVC) with a linear kernel is employed, achieving an accuracy of approximately 73.38%. The results demonstrate the potential of machine learning in facilitating early diagnosis and intervention for diabetes. Key Words: Diabetes Prediction, Machine Learning, SVC, Pima Indians Dataset, Model Evaluation, Early Diagnosis
1. INTRODUCTION Diabetes mellitus is a chronic condition that impairs the body's ability to regulate blood sugar levels. With the increasing prevalence of this disease, early diagnosis is crucial to prevent complications such as heart disease, kidney failure, and nerve damage. According to the World Health Organization, the global prevalence of diabetes has nearly quadrupled since 1980, underscoring the importance of timely diagnosis and intervention. Traditional diagnostic methods require medical tests such as blood glucose measurement, which can be both costly and time-consuming.
To evaluate the model's performance using measures such as accuracy, precision, recall, and F1-score.
To determine the significance of feature selection, data preprocessing, and feature scaling in enhancing model performance.
To compare SVC's prediction accuracy and generalization to that of other classification models (for example, logistic regression and decision trees).
To investigate the prediction model's potential for real-world applications in healthcare, specifically early diabetes identification.
Table -1: Publications Cited:
The major goal of this study is to develop an effective machine learning model for predicting diabetes using the Pima Indians Diabetes dataset. The study's particular aims are:
Impact Factor value: 8.315
Different papers and articles have been reviewed for this project. Also, their conclusions are summarized in this section. The section present documents that were studied prior and post project development. The mentioned articles provide with a better understanding about structure of the system and how various algorithms could be combined together so as to build a system with higher efficiency.
1.2. OBJECTIVES
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To create a machine learning model with the Support Vector Classifier (SVC) technique.
2. LITERATURE SURVEY
Machine learning offers an innovative alternative, enabling the development of predictive models that can classify individuals as diabetic or non-diabetic based on clinical data. The Pima Indians Diabetes dataset, which includes records of female individuals, provides a rich source of clinical features such as age, BMI, glucose levels, and insulin concentrations. These features are known to be highly correlated with diabetes risk. This study investigates the application of Support Vector Classifier (SVC) in predicting diabetes and compares the results with other commonly used machine learning models.
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Title
Year
Author
Summary
Diabetes Care and Its Risk Factors
2010, Journal Smith et Examines key of Diabetes al. factors Research influencing diabetes prevalence and progression.
Machine Learning for Diabetes Prediction
2015, International Journal of AI Research
Johnson Explores various et al. ML algorithms for diabetes
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prediction using medical data.
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