A Hybrid Software Engineering And Machine Learning Approach For Diabetes Prediction In Health Informatics
Keywords:
Diabetes Prediction, Extreme Learning Machine, Health Informatics, Machine Learning Algorithms and Data Preprocessing.Abstract
In this study, we propose an innovative framework that integrates software engineering principles with machine learning techniques for diabetes prediction in health systems. The proposed methodology, termed SEMLHI, encompasses four core modules: software engineering, machine learning algorithms, health informatics data, and prediction evaluation. By utilizing the Extreme Learning Machine (ELM) model for binary classification, we predict the likelihood of diabetes based on several health-related features such as age, blood pressure, insulin levels, and BMI. Data preprocessing techniques, including feature extraction, normalization, and missing value imputation, were employed to ensure the high quality of the dataset. Experimental results show that the ELM model significantly outperforms other machine learning models, achieving an accuracy of 92.86%. Additionally, the confusion matrix and Receiver Operating Characteristic (ROC) curve analysis highlight the model's superior performance in distinguishing between diabetic and non-diabetic patients. The results of this study provide a robust and efficient solution for diabetes prediction, which can be extended to other healthcare applications
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