A Comprehensive Review on Diabetes Disease Prediction and Risk Modeling Using Machine Learning Algorithms
Keywords:
Clinical Data, Diabetes Prediction, Healthcare, Logistic Regression, Machine Learning.Abstract
The increasing global prevalence of diabetes has intensified the demand for accurate and early diagnostic systems. Diabetes is a worldwide health issue that necessitates precise prediction techniques. This study examines research that uses clinical data and machine learning approaches to predict diabetes. Common preprocessing procedures include encoding categorical data, resolving missing values, and normalization. To improve model performance, dimensionality reduction techniques such as Principal Component Analysis (PCA) and feature selection are employed. Metrics like accuracy, precision, recall, F1-score, and AUC-ROC are used to compare supervised learning algorithms like Random Forest, Support Vector Machines (SVM), k-Nearest Neighbours (k-NN), Logistic Regression, and Decision Trees. Many research employ small datasets, which affects generalizability even while accuracy is excellent. The study emphasizes the necessity for diversified datasets and therapeutically relevant, interpretable models while highlighting shortcomings in model interpretability and validation.
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