A Fairness-Aware Comparative Analysis of Machine Learning Algorithms for Medical Diagnosis Across Diverse Patient Demographics

Abstract

AbstractThe advent of machine learning has introduced a paradigm shift in clinical decision supportsystems. This paper conducts a thorough comparative review of ML algorithms employed formedical diagnosis prediction, encompassing both traditional methods (Naïve Bayes, k-nearestneighbors, support vector machines) and sophisticated deep learning approaches(convolutional neural networks, graph neural networks). Our evaluation, grounded in ananalysis of existing literature and performance on benchmarking datasets, focuses on keymetrics of accuracy, robustness, and explainability. We further explore predominantmethodological trends and significant hurdles, including managing imbalanced data andensuring model generalizability. Finally, the review outlines a pathway toward creatingtransparent, dependable, and ethically sound AI solutions for clinical environments.Keywords: Machine Learning, Medical Diagnosis, Healthcare Analytics, PredictiveModeling, Clinical Decision Support

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