Disease Prediction System using Support Vector Machine and Multi-linear Regression

Disease Prediction System using Support Vector Machine and Multi-linear Regression

Akshata Ringe, Tejaswini Shinde, Krushnalata Zurule, Omkar Patil, Prof. Suvarna Unde

ABSTRACT- 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, Multi-linear Regression (MLR), Support Vector Machine (SVM).