Machine learning based outlier detection for medical data

R. Vijaya Kumar Reddy (Prasad V. Potluri Siddhartha Institute of Technology)
Shaik Subhani (Sreenidhi-Institute of Science and Technology (A))
B. Srinivasa Rao (Lakireddy Bali Reddy College of Engineering)
N. Lakshmipathi Anantha (Malla Reddy Engineering College (A))

Indonesian Journal of Electrical Engineering and Computer Science
Vol 24, No 1: October 2021

The concept of machine learning generate best results in health care data, it also reduce the work load of health care industry. This algorithm potentially overcome the issues and find out the novel knowledge for development of medical date in health care industry. In this paper propose a new algorithm for finding the outliers using different datasets. Considering that medical data are analytic of mutually health problems and an activity. The proposed algorithm is working based on supervised and unsupervised learning. This algorithm detects the outliers in medical data. The effectiveness of local and global data factor for outlier detection for medical data in real time. Whatever, the model used in this scenario from their training and testing of medical data. The cleaning process based on the complete attributes of dataset of similarity operations. Experiments are conducted in built in various medical datasets. The statistical outcome describe that the machine learning based outlier finding algorithm given that best accurateness.