Advancements in Machine Learning Algorithms for Predictive Analytics in Healthcare

Abstract

Recent improvements in machine learning (ML) algorithms have changed predictive analytics in healthcare in a way that has never been seen before. This means that there are now more ways than ever to improve patient results and business efficiency. This summary looks at important changes and what they mean. A lot of healthcare data, like electronic health records (EHRs), medical images, genetics, and personal sensor data, is being used more and more with machine learning methods like guided learning, unsupervised learning, and deep learning. These methods make it possible to use predictive models to diagnose diseases, give patients specific treatment suggestions, and keep track of their care. For sorting things into groups, supervised learning techniques like support vector machines (SVM) and random forests have been used to correctly spot diseases based on complicated data trends. For example, SVMs have been useful for telling the difference between different types of cancer from genetic data, which helps with focused treatments. On the other hand, unsupervised learning algorithms like grouping algorithms help find groups of patients who share similar traits, which makes personalized medicine possible. Deep learning, has been very successful in medical picture analysis, being more accurate than humans at tasks like finding tumors in x-rays and lab slides. Its ability to instantly learn traits from raw data has made diagnosis easier and more accurate. ML algorithms also help healthcare operations run more smoothly by using prediction analytics to help hospitals handle their resources better, make the best use of their staff, predict which patients will need to be admitted, and lower the number of times they have to be readmitted. These predictive models use a variety of data sources to guess how patients will do and how they will use healthcare resources, which helps people make smart decisions and make the best use of their resources.