Heart Disease Prediction System Using Machine Learning Algorithm

Journal of Computer Science, 2020

Heart disease is the number one cause of death for all communities of individuals in advanced countries and a major problem for emerging nations too. Doctors' availability to care for the general population could not catch up with the present demand for healthcare. So, there is a severe need for a support system to assist save individuals. With novel ML frameworks and big data repositories, our motive is to design a machine learning model to predict heart disease at the earliest, help prioritize hospital consultations and improve accuracy. For this study, several analyzes were carried out on the Cleveland heart disease data set with 303 patients records, using five different classifiers namely Support Vector Machine (SVM), Random forests, Ordinal Regression, Logistic Regression and Naïve Bayes. Feature selection using chi-squared statistical test and correct tuning of hyperparameters maximized classification accuracy of the Support vector machine (Radial basis function) from 40% to 85%. By incorporating rules based on the statistical patterns observed, the efficiency was further enhanced to 95%. On the other side, seeing it as a 5-class classification, multi-class imbalance issue was addressed using suitable sampling techniques that resulted in 96% accuracy for 5-class data. We evaluated model efficiency using k-fold cross validation and confusion matrix. This study shows that the classification accuracy could be significantly improved by balancing the dataset using sampling and by properly tuning hyperparameters after feature selection.

. International Journal of Computer (IJC), 34(1), 140-152, 2019

Heart as one of the essential organ of the human body and with its related disease such as cardiovascular diseases accounts for the death of many in our society over the last decades, and also regarded as one of the most life-threatening diseases in the world. Hence we seek to predict a system for Heart disease using a supervised Machine Learning (ML) trained model in MATLAB2018 workflow in a real-time environment. To develop the system, 299 heart sounds from patients were obtained and labeled as normal and abnormal heart sound. Features were extracted and labeled as dataset; K Nearest Neighbour (KNN), Support Vector Machine (SVM) and Decision Tree (DT) algorithm were used as the training platform. From the classification analysis developed using the supervised ML trained model in MATLAB2018 in conjunction with system software features for the prediction of the heartbeat for both current and predefined of a heart condition algorithms used in training the dataset for the prediction when principle component analysis was enabled, the result shows that KNN algorithm has the highest and best accuracy of 94.4%, followed by the SVM with 84.4% and DT had 81.1%. while from the evaluation analysis, KNN on Receive Operation Characteristic Curve (ROC) with 90% variance and training time of 12.88 seconds on positive class of abnormal over false classes of normal heart sound has AUC as 0.94 and on ROC curve with PCA 90% variance and training time of 1.7119 seconds on positive class of normal over negative classes of abnormal heart sound has AUC as 0.89 efficiency.-141 Hence the analysis from the result shows that out of the three classified algorithms used, KNN predicts and have the highest accuracy and is more efficient with respect to real-time environment.

LBEF RESEARCH JOURNAL OF SCIENCE, TECHNOLOGY AND MANAGEMENT, 2019

The major killer cause of human death is Heart Disease (HD). Many people die due to this disease. Lots of researchers have been discovering new technologies to prognosticate the disease early before it's too late for helping healthcare as well as people. These processes are still under research phase. Machine Learning (ML) is faster-emerging technology of Artificial Intelligence (AI) that contributes various algorithms for HD. Based on the proposed problem, ML provides different classification algorithms to divine the probability of patient having HD. For predicting HD, a lot of research scholars contributes their effort in this work using various techniques and algorithms such as Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), KNN (K-Nearest Neighbor), Neural Network (NN), etc. In order to give some effort on this work, we are going to develop a Web-based Heart Disease Prediction System (HDPS) by applying DT and NB ML algorithms. We are using the UCI repository HD dataset to train a model by comparing DT and NB algorithm for HDPS Web application. The dataset contains 303 instances with 14 attributes that help to train a prediction model that will be deployed into a web application for prediction. The main aim of this project is to build an efficient prediction model and deploy for prediction of disease. An HDP Model is built by using NB algorithm that provides 88.163% accuracy among others. A web-based HDPS application is developed through the waterfall model. Each phase is efficiently done. The project is successfully created with help of requirement analysis and project plan, system design, database design, testing plan, identifying features and functionalities, and system validation and deployment. The limitation of this project is to have only predicted the presence of heart disease but not identify which type of HD does have at patient. In future work, we can enhance the project by appending more detail prediction of HD at patient and incorporate with smart wear devices that integrate to Hospital Emergency System.

VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE

Heart disease is a common problem which can be very severe in old ages and also in people not having a healthy lifestyle. With regular check-up and diagnosis in addition to maintaining a decent eating habit can prevent it to some extent. In this paper we have tried to implement the most sought after and important machine learning algorithm to predict the heart disease in a patient. The decision tree classifier is implemented based on the symptoms which are specifically the attributes required for the purpose of prediction. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. The decision tree algorithm works in a way where it tries to solve the problem by the help of tree representation. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. The support vector machine algorithm helps us to classify the datasets on the basi...