Heart Disease Prediction System Using Decision Tree and Naive Bayes Algorithm

Abstract

The huge information of healthcare data is collected from the healthcare industry which is not “mined” unfortunately to make effective decision making for the identification of hidden information. The end user support system is used as the prediction application for the heart disease and this paper proposes windows through the intelligent prediction system the instance guidance for the heart disease is given to the user. Various symptoms of the heart diseases are fed into the application. The user precedes the processes by checking the specific detail and symptoms of the heart disease. The decision tree (ID3) and navie Bayes techniques in data mining are used to retrieve the details associated with each patient. Based on the accurate result prediction, the performance of the system is analyzed.

Predictability of postoperative recurrence on hepatocellular carcinoma through data mining method

“…The representation of data in the form of the tree is easily understood and this method can handle multidimensional data. Recently, the data mining method has been reported in the field of heart disease (15).…”

Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis

“…NB is a simple classifier that predicts and assigns class labels to external data based on vectors of descriptors for a finite set of training observations. The NB classifier posits that each descriptor contributes independently to the probability that an observation belongs to a specific class (e.g., disease or no disease) [ 37 – 40 ]. The chance of an observation belonging to a specific class is calculated by multiplying the individual probabilities of that class within each individual descriptor [ 37 – 40 ].…”

“…The NB classifier posits that each descriptor contributes independently to the probability that an observation belongs to a specific class (e.g., disease or no disease) [ 37 – 40 ]. The chance of an observation belonging to a specific class is calculated by multiplying the individual probabilities of that class within each individual descriptor [ 37 – 40 ]. We implemented NB learner node within KNIME Analytics Platform with the following parameters: default probability = 0.0001, minimum standard deviation = 0.0001, threshold standard deviation = 0.0 and maximum number of unique nominal values per attribute = 20.…”

“…Supervised ML tools can be utilized to build predictive models involve the implementation of statistical means for learning and predicting disease status, either by including or excluding the polymorphisms genotypes [ 5 , 31 – 33 ]. The following are popular ML algorithms that were evaluated in the current research to predict T2DM and dyslipidemia based on the clinical parameters, demographic and polymorphism data: random forest (RF) [ 34 – 36 ]; naïve Bayesian (NB) [ 37 – 40 ]; eXtreme Gradient Boosting (XGBoost) [ 41 – 43 ]; k-nearest neighbors (kNN) [ 44 – 46 ], support vector machine (SVM) [ 47 , 48 ]; probabilistic neural networks (PNN) [ 49 – 53 ]; multilayer perceptron (MLP) [ 54 , 55 ]; adaptive boosting (AdaBoost) [ 56 , 57 ]; gradient boost [ 58 , 59 ]; and K-star (K*) [ 60 , 61 ]. It was reported that the odds ratio for each T2DM risk allele varies between 1.02 and 1.35.…”

A Machine Learning Approach: Using Predictive Analytics to Identify and Analyze High Risks Patients with Heart Disease

“…The implementation of the Naï ve Bayes and Decision Tree algorithms to predict heart diseases was studied in another research [10], the results of their model showed that Naï ve Bayes is more powerful in term of accuracy than Decision Tree.…”