Paper Title
PREDICTION OF GENETIC DISORDER (JPA AND MED) THROUGH DNA SEQUENCE USING MACHINE LEARNING CLASSIFIERS

Abstract
Abstract - DNA is a molecule that houses the biological instructions that distinguish each species from the others. Adult organisms pass on their DNA and the instructions it contains to their progeny. Due to immunological deficiencies and other environmental factors, a variety of diseases and disorders can impact the human body and can change DNA strands. By using machine learning techniques and training the system to anticipate irregularities in human DNA, DNA can be analyzed. Healthcare is one of the key industries where machine learning is being applied. Most of the research and medical facilities work to develop themselves in order to make more informed decisions regarding patient diagnosis and care.Predictive models based on machine learning enable us to process intricate medical data and offer improved disease diagnosis prognoses. As a result, a variety of fatal and serious illnesses are identified, enabling medical practitioners to begin treatment sooner. Any forecast of the illness would accelerate treatment, preventing the last stages or possibly aiding in the identification of a potential outbreak. To better understand how to apply three different classifiers to a genetic dataset derived from a gene microarray and compare the outcomes to see which yields higher accuracy, the focus of this study is working with the dataset. Keywords - Decision Tree Classifiers (DTC), Disease prediction, Extra Tree Classifier (ETC), Genetic microarray data, K Nearest Neighbor Classifier (KNC).