Disease Prediction System using Support Vector Machine and Multilinear Regression (International Journal of Innovative Research in Computer Science & Technology, 2020)
Evolution of modern technologies like data  science and machine learning has opened the path for  healthcare communities and medical institutions, to detect  the diseases earliest as possible and it helps to provide better  patient care. Accuracy of detecting the possible diseases is  reduced when we do not have complete medical data.  Furthermore, certain diseases are region-based, which  might cause weak disease prediction. Our body shows the  symptoms when something wrong is happening within our  body, sometime it may be just minor problem but sometimes  we can have severe illness and if we do not take care of these  symptoms at the early stage then it might be too late to cure  the disease. So we are proposing a disease prediction system  that can predict the possible diseases based on symptoms so  it can be cured at the early stage. It saves time that is  required to do the complete diagnosis of the patient and  based on the suggestions provided by the system we can  only get the patient diagnosed for those diseases that are  required. In this paper, we are using machine learning  algorithms that try to accurately predict possible diseases.  The results generated by the proposed system have an  accuracy of up to 87%. The system has incredible potential  in anticipating the possible diseases more precisely. The  main motive of this study is to help the nontechnical person  and freshman doctors to make a correct opinion about the  diseases.
Keywords: Disease Prediction System; Machine Learning; Multilinear Regression (MLR); Support Vector Machine (SVM)