Outlier Identification Based on Machine Learning for Medical Equipment

Abstract

In real-time medical databases, feature selection methods are crucial. The majority of medical databases have high dimensionality and enormous amounts of data, making it challenging to identify a crucial key feature using conventional feature sub-set selection methods. Additionally, due to the high data size and feature space, typical medical data filtering algorithms fall short of identifying the crucial outliers. The use of machine learning in the healthcare industry not only produces the best outcomes but also lessens the workload. This algorithm might solve the problems and uncover fresh information for the advancement of medicine in the healthcare sector. The new approach for locating outliers using various datasets is proposed in this research. Taking into account that medical data analyse both a health issue and an activity. Based on both supervised and unsupervised learning, the suggested method operates. The outliers in medical data are found using this approach. The efficiency of using local and global data factors to quickly identify outliers in medical data. Whatever the case, the model in this scenario was created by them and tested using medical data. the cleaning procedure using all of the dataset's properties for similarity operations. Various medical datasets with built-in experimentation are used. The statistical results show that the outlier identification technique based on machine learning has the highest level of accuracy.