Abstract

Abstract: With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits earlydisease 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, whichmay weaken theprediction of disease outbreaks. In this paper, we streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities. The application of machine learning in the field of medical diagnosis is increasing gradually. This can be contributed primarily to the improvement in the classification and recognition systems used in disease diagnosis which is able to provide data that aids medical experts in early detection of fatal diseases and therefore, increase the survival rate of patients significantly.The results of the study strengthen the idea of the application of machine learning in early detection of diseases. Compared toseveral typical calculating algorithms, the scheming accuracy of our proposed algorithm reaches 94.8% with a regular speed which is quicker than that of the unimodel disease risk prediction algorithm and produces report.Keywords: Machine Learning, Django, Navie Bayes, HealthCare Symptoms.