Volume 19 • Issue 2 • PP: 28-44 • 2025
Innovations in Health Anomaly Detection: A Comparative Review of Machine Learning and Statistical Approaches
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One of the significant challenges in modern healthcare is the early and accurate detection of health anomalies, especially in the case of life-threatening diseases such as breast cancer. This paper investigates the comparative efficacy of ML models and statistical methods for the classification of breast tumors as benign or malignant using the Breast Cancer Wisconsin (Diagnostic) Dataset. The dataset, comprising various tumor cell attributes, was preprocessed with Principal Component Analysis (PCA) to enhance model training efficiency. The first 11 principal components retained 95% of the total variance, ensuring minimal information loss while reducing dimensionality. We compared the performance of several machine learning algorithms, including Logistic Regression, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Decision Trees (DT), Random Forests (RF), Naïve Bayes (NB), and K-Nearest Neighbors (KNN). Among them, Logistic Regression, SVM, and ANN achieved near-perfect classification accuracy with balanced precision-recall metrics, where the accuracy rates were all more than 98%. XGBoost and Random Forest were also very impressive as advanced models, while simple models like Decision Trees and Naïve Bayes proved to be less potent and were unable to manage class imbalances and complex data patterns. Our main findings are essentially reflective of the transformative role machine learning would play in healthcare; for instance, enhancing the accuracy of diagnosis, optimizing clinical workflow, and promoting decision-making. These insights are made actionable for practitioners in healthcare to promote the adoption of reliable ML solutions for breast cancer detection. In the future, real-time data integration, external validation, and hybrid modeling approaches must be considered to further enhance the practical utility of these findings.
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