International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025
p-ISSN: 2395-0072
www.irjet.net
Early Detection of Diabetes Using Machine Learning Techniques 1Ebrahim Abdul Monem Ayoub
Master Student at Syrian Virtual University Homs, Syria ebrahim_210362@svuonline.org
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Abstract - Artificial intelligence has become a pivotal tool
2030 and 783 million by 2045. Diabetes caused the death of 6.7 million people, and diabetes is the seventh leading cause of death in the United States.
in enhancing healthcare, significantly contributing to the advancement of diagnostic methods and medical decisionmaking. Traditionally, medicine relied primarily on the expertise of physicians. However, with the massive increase in medical data, the need for AI applications has grown, making machine learning more prevalent in the medical field.
In the face of this increasing challenge, the urgent need has emerged to develop effective methods for the early detection of diabetes. Herein lies the importance of using artificial intelligence and machine learning techniques as advanced tools in diagnosis. These technologies enable the analysis of medical data with high accuracy, facilitating early disease detection and aiding in taking appropriate preventive measures in a timely manner.
Diabetes is one of the most common chronic diseases and presents a significant challenge due to its impact on various bodily functions. Irregular blood glucose levels can lead to severe complications, such as heart disease or eye diseases (diabetic retinopathy). Therefore, early detection of this condition is a crucial step in preventing complications and reducing health risks for patients.
This research seeks to provide a scientific contribution in this field by leveraging machine learning techniques to analyze health data and develop accurate models for early diagnosis.
In this research, we apply machine learning algorithms to predict diabetes while focusing on the impact of data balancing using the SMOTE technique on model performance. Several algorithms were tested, including Support Vector Machine (SVM) and Random Forest (RF), where the results showed a significant improvement in classification accuracy after addressing data imbalance. Additionally, an interactive web-based model was developed, allowing doctors and patients to input their health data and receive reliable diabetes risk predictions. This study highlights the importance of feature analysis and the use of hyperparameter tuning techniques to ensure better accuracy, with recommendations to expand research to include reinforcement learning and the Internet of Things (IoT) for enhanced diabetes monitoring and diagnosis.
1.1 Diabetes Mellitus It occurs either when the pancreas does not produce enough insulin (insulin is a hormone that regulates blood sugar) or when the body cannot effectively use the insulin it produces , leading to damage, dysfunction, and failure of various organs [2].
1.2 Machine Learning Machine learning is a branch of artificial intelligence that enables computer systems to learn automatically without human intervention. It focuses on using data and algorithms rather than relying on human skills and abilities, which provides greater accuracy and speed.
Key Words: Machine Learning Algorithms, Data Analysis, Diabetes Mellitus, Classification, Feature Selection, Data Balancing.
2. RELATED WORK Dr. Nasreen Suleiman [3] presented a model for predicting type 2 diabetes using machine learning algorithms. Two models were developed based on Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR). The models were evaluated using the Pima Indian Diabetes dataset. The results showed that XGBoost outperformed the LR model, achieving an AUROC of 85%, sensitivity of 71%, specificity of 81%, accuracy of 77%, precision of 67%, and an F1-score of 69%.
1.INTRODUCTION The spread of diseases threatens health, economy, and security, which requires us to give diseases of all kinds a lot of time and effort to avoid their risks. There are many diseases that deserve to be at the forefront of studies for their diagnosis and treatment. In this research, we will focus on diabetes. Diabetes is a major public health problem that is increasing significantly. According to the International Diabetes Federation [1], in 2021, about 537 million adults (20-79 years) are living with diabetes. The total number of people with diabetes is expected to rise to 643 million by
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