Prediction System for Heart Disease Based on Ensemble Classifiers (original) (raw)

An Ensemble Classifier for the Prediction of Heart Disease

Heart disease has become a silent killer among people of all ages. The major risk factors of heart disease include smoking, blood pressure, cholesterol, diabetes etc. Early diagnosis and treatment can reduce morbidity rate to an extent by identifying patients at higher risk of having a heart disease and providing them right care at right time. However provisioning of quality services at reasonable costs is a major concern of every healthcares. Poor clinical decisions can pose adverse effects on human health. This paper introduces a method based on data mining according to the information of patients' medical records to predict heart disease. An ensemble classifier approach is being used, that is the combination of three classifiers (KNN, Decision Tree, NaiveBayes) composing an ensemble, so that the overall model can be used to give predictions with greater accuracy than that of individual classifiers.

CARDIOVASCULAR DISEASE PREDICTION USING ENSEMBLE CLASSIFICATION ALGORITHM IN MACHINE LEARNING

ICTACT Journal on Soft Computing, 2022

Cardiovascular disease includes a wide range of heart-related illnesses and has surpassed cancer as the top cause of mortality worldwide in recent decades. Many people nowadays are engrossed in their daily lives and engage in various activities while ignoring their health. As a result of their rushed lifestyles and disrespect for their health, the number of people becoming unwell is increasing every day. According to the World Health Organization, heart disease claims the lives of over 31% of the world's population. As a result, doctors must be able to predict whether a patient may develop heart illness, but the amount of data collected by the medical sector or hospitals, on the other hand, is so vast that it can be difficult to analyze at times. This research paper assessed several aspects of heart illness and develops a model based on supervised learning methods like Gaussian Naïve Bayes and AdaBoosting algorithm. The purpose of this research is to figure out how to anticipate whether a patient will develop heart disease. The AdaBoosting algorithm achieves a great accuracy score of 95%, according to the data.

Ensemble technique to predict heart disease using machine learning classifiers

Network Biology, 2023

The exact forecast of heart disease is necessary to proficiently treat cardiovascular patients before a heart failure happens. Assuming we talk about AI techniques can be accomplished utilizing an ideal AI model with rich medical services information on heart diseases. To begin with, the feature extraction technique, gradient boosting-based sequential feature selection (GBSFS) is applied to separate the significant number of features (5, 7, 9, and 11) from coronary illness dataset to create important medical services information. The stacking model is prepared for coronary illness forecast. A comparison model is made between datasets with prominent features (5, 7, 9, and 11) as well as all features. The proposed framework is assessed with coronary illness information and contrasted and customary classifiers in view of feature elimination include determination strategies. The proposed framework acquires test accuracy of 98.78%, which is most noteworthy in marking model with 11-featuers and higher than existing frameworks. This outcome shows that our framework is more powerful for the expectation of coronary illness, in contrast with other cutting edge strategies.

An ensemble based decision support framework for intelligent heart disease diagnosis

International Conference on Information Society (i-Society 2014), 2014

Large amount of medical data leads to the need of intelligent data mining tools in order to extract useful knowledge. Researchers have been using several statistical analysis and data mining techniques to improve the disease diagnosis accuracy in medical healthcare. Heart disease is considered as the leading cause of deaths worldwide over the past 10 years. Several researchers have introduced different data mining techniques for heart disease diagnosis. Using a single data mining technique shows an acceptable level of accuracy for disease diagnosis. Recently, more research is carried out towards hybrid models which show tremendous improvement in heart disease diagnosis accuracy. The objective of the proposed research is to predict the heart disease in a patient more accurately. The proposed framework uses majority vote based novel classifier ensemble to combine different data mining classifiers. UCI heart disease dataset is used for results and evaluation. Analysis of the results shows that the sensitivity, specificity and accuracy of the ensemble framework are higher as compared to the individual techniques. We obtained 82% accuracy, 74% sensitivity and 93% specificity for heart disease dataset.

Development of ensemble model for heart disease diagnosis

UTB, 2023

Development of an ensemble model for diagnosing heart diseases has become increasingly popular in recent years due to its ability to enhance the accuracy and robustness of traditional machine learn- ing models. This study presents the development and evaluation of an ensemble model for diagnosing heart diseases. The proposed model utilizes several machine learning algorithms, including KNN, logistic regression, multilayer perceptron (MLP_ANN), and support vector machines, to combine the strengths of each algorithm and achieve more accurate results. Feature selection methods, such as chi-square and information gain, are employed, and the performance of the model is evaluated using standard metrics. The results demonstrate that the ensemble model outperforms individual traditional models and achieves higher accuracy in diagnosing heart diseases. The ensemble model also exhibits improved robustness, which is crucial for real-world applications. This work provides valuable in- sights for the potential development of more efficient tools for diagnosing heart diseases in the future.

HEART DISEASE PREDICTION USING ENSEMBLE APPROACH

Journal ijetrm, 2024

A heart attack, also known as a myocardial infarction, occurs when the flow of blood to a part of the heart muscle is suddenly blocked. This blockage prevents the heart from receiving enough oxygen. If blood flow is not promptly restored, the heart muscle begins to die. Machine learning algorithms are a powerful tool for predicting heart disease, allowing for more accurate results than traditional methods. Different models are compared and evaluated to identify the most accurate algorithm. Early detection is key to managing heart disease, and machine learning can help to identify at-risk individuals. Prevention of heart disease is invaluable for saving lives and reducing medical costs. Machine learning algorithms can provide the most accurate predictions possible, offering the best chance of preventing serious health issue

Hybrid Classification Using Ensemble Model to Predict Cardiovascular Diseases

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Machine Learning is a widely used tool in the healthcare industry. Machine Learning algorithms help to predict and detect the presence of cardiovascular diseases. Such information, if predicted ahead of time, can provide important knowledge to doctors who can then diagnose and deal per patient basis. We work on predicting possible heart diseases in people using Machine Learning algorithms. In this project we perform the comparative analysis of classifiers like Naïve Bayes, SVM, Logistic Regression, Decision trees and Random Forest and we propose an ensemble classifier which perform hybrid classification by taking classifiers(strong and weak) since it can have multiple number of samples for training and validating the data so we perform the analysis of existing classifier and proposed classifier like Ada-boost and XG-boost combined with logistic regression and which can give the better accuracy and predictive analysis.

Enhancing Heart Disease Prediction Using Ensemble Techniques

SLU Journal of Science and Technology

Background: Cardiovascular diseases are recognized generally to be among the number one illnesscausing death across the globe. Predicting heart disease using a computer-aided technique makes it easier for medical practitioners to diagnose and thereby savinglives andreducingcosts. Feature selection has become an essential component for developing Machinelearning models. It chooses the most relevant features from the available dataset,thereby shortening the training period, making the model easier to train, improving generalization and decreasing overfitting without necessarily compromising the system’s accuracy. Aim:The purpose of this work is to design and build an optimal model forthe prediction of heart diseases,especially at an early stage by considering certain features that are most relevant forthe prediction without compromising the system’s accuracy. Method: The Cleveland UCI dataset with 303 instances wereused in trainingthe model and the findings showthat selectKBest is an ...

Performance of Hybrid Ensemble Classification Techniques for Prevalence of Heart Disease Prediction

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

In medical science, heart disease is being considered as fatal problem and in every seconds most of the people dies due to this problem. In heart disease, typically heart stops blood supply to other parts of the body. Hence, proper functioning of body stopped and affected. In this way, timely and accurate prediction of heart disease is an important concern in medical science domain. Diagnosing of heart patients with previous medical history is not being considered as reliable in many aspects. However, machine learning techniques have mystery to classify heart disease data efficiently and effectively and provide reliable solutions. In the past, prediction of heart disease problem various machine learning tools and techniques have been adopted. In this study, hybrid ensemble classification techniques like bagging, boosting, Random Subspace Method (RSM) and Random Under Sampling (RUS) boost are proposed and performance is compared with simple base classification techniques like decisio...

An Effective Heart Disease Prediction Framework based on Ensemble Techniques in Machine Learning

International Journal of Advanced Computer Science and Applications

To design a framework for effective prediction of heart disease based on ensemble techniques, without the need of feature selection, incorporating data balancing, outlier detection and removal techniques, with results that are still at par with cutting-edge research. In this study, the Cleveland dataset, which has 303 occurrences, is used from the UCI repository. The dataset comprises 76 raw attributes, however only 14 of them are listed by the UCI repository as significant risk factors for heart disease when the dataset is uploaded as an open source dataset. Data balancing strategies, such as random over sampling, are used to address the issue of unbalanced data. Additionally, an isolation forest is used to find outliers in multivariate data, which has not been explored in previous research. After eliminating anomalies from the data, ensemble techniques such as bagging, boosting, voting, stacking are employed to create the prediction model. The potential of the proposed model is assessed for accuracy, sensitivity, and specificity, positive prediction value (PPV), negative prediction value (NPV), F1 score, ROC-AUC and model training time. For the Cleveland dataset, the performance of the suggested methodology is superior, with 98.73% accuracy, 98% sensitivity, 100% specificity, 100% PPV, 97% NPV, 1 as F score, and AUC as 1 with comparatively very less training time. The results of this study demonstrate that our proposed approach significantly outperforms the existing scholarly work in terms of accuracy and all the stated performance metrics. No earlier research has focused on these many performance parameters.