Because of the rapid population growth in diseases in recent years, early disease identification has become a critical problem. With massive population growth, the probability of death from breast cancer is increasing exponentially. Breast Cancer is the second leading most severe of the cancers that have already been identified, and following this, others are Thyroid and Polycystic Ovary Syndrome (PCOS) disease. An automatic disease diagnosis system assists medical personnel in disease detection, provides dependable, effective, and immediate responses, and reduces the death risk. In this paper, we study different machine learning techniques and their utility in predicting breast cancer, thyroid and PCOS to get the best result. The performance measures such as accuracy, specificity, sensitivity, precision, recall, F1 score, and receiver operating curve are discussed to assess the performance of machine learning algorithms. The paper explores the research work using machine learning algorithms done in detecting breast cancer, thyroid, and PCOS.

Keywords : Breast Cancer, Thyroid, PCOS, Dataset, Machine Learning