From Data to Diagnosis: A Comprehensive Review of Machine Learning in Healthcare Systems

Abstract

ML (machine learning) is revolutionizing healthcare by allowing data-driven advancements in diagnosis, planning treatment, predicting risk, and keeping an eye on patients.  The review tries to look at how ML has changed over time, what it is used for, how it works, how to measure its performance, and where it might go in the future in healthcare.  The study uses a qualitative literature review method to look at results from supervised learning, deep learning, and predictive modelling techniques from a number of peer-reviewed sources.  Diagnostic decision support, predictive analytics, personalized medicine, medical imaging, and remote patient tracking are some of the most important uses that have been named.  A review of these models shows that they are very accurate, precise, and useful in clinical settings, especially when using methods like LSTM and CNNs.  To check for robustness and generalizability, performance measures like F1-score, AUC-ROC, and cross-validation were always used.  But challenges with data quality, interpretability, ethics, and legal gaps still make it hard for many people to use.  ML has a bright future in healthcare, especially when IoT, digital twins, big data, and NLP are all used together to help with personalized, preventative, and effective care.  This review shows how important it is for everyone to work together to fix the challenges that are happening now and fully use ML's potential for transforming healthcare.