Identification of Medical Outliers with the use of Machine Learning

Abstract

Machine learning has the potential to not only improve outcomes in healthcare data, but also to lessen the burden on healthcare workers. This algorithm has the potential to aid in the resolution of problems and the identification of new information crucial to the development of medical data. As part of our study, we provide a novel method for identifying outliers across many data sets. if one considers the possibility that information gleaned from medical records might shed light on the dynamics at play in an illness or person’s way of life. The presented approach uses a combination of supervised and unsupervised learning to achieve its goals. It’s probable that this programme might detect any discrepancies in the patient’s medical file. How well regional and international data sources cooperate to detect anomalies in medical records in real time. The model is used here regardless of whether or not it was trained and verified using actual patient data. The cleaning method takes full use of all the advantages of analogous processes. The utilisation of medical data sets in research is common. The arithmetical results demonstration that the machine learning-based approach to outlier identification is more accurate.