Streamlit Powered Multi-Disease Prediction with Machine Learning

Abstract-Machine learning techniques are doing wonders in every sphere of life but using predictive analysis in healthcare is a challenging task. However, if implemented properly these techniques help in making timely judgements about the health and treatment of patients. Globally, diseases including diabetes, heart disease, and breast cancer are major causes of death; yet, the majority of these deaths are due to failure to have regular checkups for these conditions. Low doctor-to-population ratios and a lack of medical infrastructure are the root causes of the above-mentioned issue. Thus, early detection and treatment of these diseases can save many lives. Machine Learning, Deep Learning and Streamlit is an effort concentrated on the development of healthcare using in-depth engines to forecast several sicknesses. Streamli Cloud and Streamlit Library facilitate deployment of prediction models like a breeze for developers. This has made accessing and using prediction capabilities of the system easily done by any layman. The paper focuses on forecasting three major diseases namely diabetes, heart failure and Parkinson’s disease by using an advanced ensemble of deep learning models as well as traditional machine learning techniques. Then again, merging Support Vector Machine (SVM) algorithm together with Logistic Regression models will form one such integration scheme.