Analysis, Design and Development of a Predictive Model to Predict Coronary Attorney of Human Body using Data Mining Algorithm

Abstract

Data mining have used worldwide for mining information from different types of database and data mining techniques are quite popular in medical sectors. Nowadays coronary attorney is the most leading cause of death according to many surveys all over the world which also can be predicted by data mining. The main goal of this research is to predict and classify coronary attorney by designing predictive and classification model. Authors have developed a system which contains both predictive and classification model. Predictive model have designed for predicting the coronary attorney. Authors have used j48 algorithm for this model. The system will show the result of inserted test data. There would be two categories to express the result and they are -positive and negative. Positive would mean that the patient have the probability of having coronary attorney and negative would mean that they don’t have the probability of having coronary attorney. Naive Bayes have applied for classification and authors have classified the dataset on the basis of Age, Diabetes and LDL of patients report. Authors have fixed Null, Low, Medium and High categories for expressing the probable state of coronary attorney. Here the prediction of coronary attorney has gained 86% accuracy by j48 algorithm. Here the classification of coronary attorney has gained 86.5% accuracy by Naïve Bayes algorithm. This research has fulfilled all the objective of this research paper but there were some limitations too. Here authors have worked with lots of test reports, physical condition and habit of a patent but it’s not able to express the exact reason which is actually responsible for coronary attorney in a patient body and authors also didn’t create any sector in this system where patients could know about the treatment or guideline of each probable stage of coronary attorney.