Prediction System for Heart Disease Using Naive Bayes

Catelyaurrently data mining is no stranger to the word of statistics, the healthcare industry collects huge amounts of healthcare data which, unfortunately, are not "mined" to discover hidden information for effective decision making. Discovery of hidden patterns and relationships often goes unexploited.  Advanced data mining techniques can help remedy this situation. 

This research has developed a prototype Heart Disease Prediction System (HDPS) using data mining techniques, namely, Decision Trees, Nave Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. HDPS can answer complex what if queries which traditional decision support systems cannot. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease.

First of all, I think Nave Bayes is an algorithm that is often used for document classification, detection and filtering of spam. There are several advantages and disadvantages when using Algorithms. One of the advantages of using this method is that it only requires a small amount of data to determine the parameter estimates required in the classification process. The main characteristic of the Naive Bayes Algorithm is that it has a very strong assumption of the independence of each condition. The basic concept used by Naive Bayes is the Bayes Theorem, which is a statistical theorem for calculating odds.

In addition The main objective of this research is to develop a prototype Health Care Prediction System using, Naive Bayes.The System can discover and extract hidden knowledge associated with diseases (heart attack, cancer and diabetes) from a historical heart disease database. It can answer complex queries for diagnosing disease and thus assist healthcare practitioners to make intelligent clinical decisions which traditional decision support systems cannot. By providing effective treatments, it also helps to reduce treatment costs. To enhance visualization and ease of interpretation, it displays the results in tabular and PDF forms.

From all of it, I believe the Naive Bayes method can predict heart disease accurately and is the most effective model for predicting patients with heart disease. This model can answer complex questions, each with its own strengths with regard to ease of model interpretation, access to detailed information and accuracy.