A Method for Forecasting Heart Disease Using Effective Machine Learning Algorithms
With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early Heart disease detection, patient care, and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. In this paper, we streamline machine learning algorithms for effective prediction of Heart disease outbreak in disease-frequent communities. We experiment the modified prediction models over real-life hospital data collected from different parts of county. To overcome the difficulty of incomplete data, we use a latent factor model to reconstruct the missing data. We experiment on a Heart disease based on the symptoms given by the user. It predicts using machine learning algorithms. So, the output is accurate. It uses flask web frame work for GUI. In this we will analyse data using ML algorithms.