Multiple Disease Prediction Using Machine Learning

Numerous machine learning approaches can do predictive analytics on vast volumes of data across a range of sectors. Although it is a challenging endeavor, predictive analytics in healthcare might ultimately help professionals make prompt judgments about patients' health and care based on vast amounts of data. Many people die from diseases including diabetes, breast cancer, and heart-related conditions worldwide, but the majority of these deaths are brought on by a failure to get regular checkups. A poor doctor-to-population ratio and a lack of medical infrastructure are the causes of the aforementioned issue. The data unequivocally demonstrate this; the WHO recommends a doctor-to- patient ratio of 1:1000, whereas India's doctor-to-population ratio is 1:1456, indicating a physician shortage. Diabetes, cancer, and heart disease can all pose a threat to humanity if they are not detected in time. Thus, many lives can be saved by early detection and diagnosis of these illnesses. The main goal of this effort is to use machine learning classification algorithms to anticipate dangerous diseases. Diabetes, heart disease, and breast cancer are all covered in this study. Our team created a medical test web application that uses machine learning to forecast various ailments in order to make this work smooth and accessible to the general audience. Our goal in this project is to create a web application that predicts numerous diseases, such as diabetes, heart disease, and breast cancer, using the idea of machine learning. Key words: Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Diabetes, Breast cancer, Heart diseases