INVESTIGATION STUDY ON HEART DISEASE PREDICTION WITH PATIENT HEALTHCARE DATA

Abstract

           Bigdata comprises the structured, semi-structured and unstructured data collected by organization mined for the predictive analytics. Heart disease is the common disease that caused the peoples worldwide. Early heart disease prediction is an essential process for diverse healthcare providers to save their lives. Heart disease prediction is carried out with signs, symptoms and physical examination of patient. Data pre-processing, feature selection and classification process are performed for efficient heart disease prediction. Data pre-processing is carried out to refill the missing values in the input database. The feature selection process is performed to choose the relevant features from pre-processed data. The classification process is performed to classify the input data as normal or abnormal data for performing heart disease prediction. Many researchers carried out their research on the heart disease prediction. But, the accuracy level was not increased and time consumption was not minimized during the heart disease prediction. In order to address these problems, existing heart disease prediction method was reviewed.

Keywords: Big data, predictive analytics, heart disease prediction, feature selection, relevant features, classification process