Comparative Analysis of Different Clinical Detection Technique for Down Syndrome Babies Using Machine Learning Algorithm

Abstract

Machine learning (ML) algorithms have potentially revolutionized a number of industries, including healthcare. In recent years, the healthcare sector has paid close consideration to the potential of ML to improve diagnosis, prognosis, and treatment planning for a number of illnesses, including Down Syndrome (DS). These techniques may analyze complex medical data, identify patterns and trends, and provide medical professionals and DS families with useful information. This paper presented researches conducted based on machine learning techniques for analysing different clinical detection techniques for DS babies. The paper also presented a proposed Improved Particle Swap Optimization (IPSO) technique for clinical detection technique for DS babies. The IPSO is expected to provide better technique for clinical detection technique for DS babies when implemented.