Integrating Machine Learning and Statistical Approaches to Improve Diagnostic Accuracy in Clinical Practice

Abstract

The integration of machine learning (ML) algorithms and statistical modeling has emerged as a transformative approach in clinical diagnostics, offering significant potential to enhance diagnostic accuracy, efficiency, and consistency. This paper explores the application of supervised and unsupervised ML techniques, in conjunction with traditional statistical methods, to identify patterns and correlations within complex clinical datasets. By leveraging electronic health records, imaging data, and laboratory results, ML models can detect subtle indicators of disease that may be overlooked by conventional diagnostic methods. Furthermore, statistical validation ensures model reliability, interpretability, and clinical relevance. Case studies in oncology, cardiology, and infectious diseases demonstrate how this integrated approach supports earlier detection, risk stratification, and personalized treatment planning. Despite promising results, challenges remain in data quality, model transparency, and clinical adoption. This study underscores the need for collaborative efforts between data scientists, clinicians, and healthcare institutions to ensure responsible and effective deployment of ML-driven diagnostics. Ultimately, the synergy between machine learning and statistical approaches offers a path toward more accurate, data-informed clinical decision-making. In this research, a Long Short-Term Memory (LSTM) deep learning model is utilised to assess prediction accuracy, demonstrating its superiority over the Bayes model. This optimised feature selection process is achieved through the use of a Ranking-based Bee Colony method, which also improves classification accuracy. A deep learning approach for predicting breast cancer is shown, which manages database noise well.