Review on Healthcare Quality Using Machine Learning Methods

Abstract

Machine learning methods have emerged as powerful tools for enhancing healthcare quality by leveraging large datasets and identifying patterns that can inform decision-making and improve patient outcomes. This review explores the application of machine learning in healthcare quality, focusing on areas such as disease diagnosis and prognosis, personalized treatment planning, predictive analytics, fraud detection, and remote care. Machine learning algorithms have demonstrated promising results in disease detection, enabling early and accurate diagnoses. By analyzing patient data, including medical images, electronic health records, and genetic information, these algorithms can provide insights into disease progression and prognosis. Moreover, medical history, and treatment response data, leading to improved treatment effectiveness and reduced adverse events. Predictive analytics techniques facilitate the identification of patients at risk of adverse events, enabling proactive interventions to prevent complications and improve patient safety.

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