Machine Learning Algorithms Applied to Improve Pattern Recognition in Disease Diagnosis

Abstract

The use of machine learning (ML) algorithms is a third methodology that breaks new ground in disease diagnosis, as it has the capability of increasing ability to identify patterns necessary for early and accurate medidetection of medical conditions The patterns and correlations that cannot be seen when using conventional disease diagnosis techniques are based on analysis of high-dimensional and heterogeneous data; therefore, ML models can define such patterns and correlations. In medical imaging, convolutional neural networks (CNNs) have achieved breakthrough results, such as outperforming expert radiologists in diagnosing diabetic retinopathy during eye examinations. The Medical AI Marketplace has been utilized to enhance and add predictive value to electronic health records (EHRs), and neural networks have demonstrated utility in analyzing sensor data from wearable devices. Besides, since healthcare professionals encounter time-consuming issues when carrying out complex procedures such as lesion detection, organ segmentation, and disease classification, ML has developed computer-aided diagnosis (CAD) devices, which automate these interventions and therefore support healthcare professionals in making patient-care decision-making. Recent advances in data fusion, fuzzy logic, and explainable AI (XAI) frameworks have enabled enhancements in the explainability, stability, and generalization capacity of ML-based diagnostic systems. However, issues such as a lack of sufficient data, class imbalance, privacy concerns, and algorithm bias continue to hinder the wide-scale implementation in the clinical environment. The empirical history of ML algorithms, their application in disease diagnosis, and current trends in feature extraction, data preprocessing, and model testing will also be explored in the given paper.

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