Heart Disease Prediction Model Research Articles

Optimization Heart Disease Prediction using Machine Learning Models

Healthcare is currently one of the most pressing global issues, with an increase in the incidence of cardiac disease affecting all age groups, particularly the young. Rapid identification and treatment of heart problems can potentially save lives. Artificial intelligence has the potential to significantly aid in this effort. In this study, we aimed to develop a heart disease prediction model using machine learning techniques. We utilized several models, including Support Vector Machine (SVM), K-Neighbors Classifier, Random Forest Classifier, Decision Tree, and Logistic Regression. Based on our experiments, the logistic regression and K-NN models produced the best results, with accuracies of 0.95592% and 0.956194%, respectively. Our findings suggest that machine learning models can be optimized for heart disease prediction and have the potential to improve healthcare outcomes.

An Ensemble Approach for Prediction of Cardiovascular Disease Using Meta Classifier Boosting Algorithms

There are very few studies are carried for investigating the potential of hybrid ensemble machine learning techniques for building a model for the detection and prediction of heart disease in the human body. In this research, the authors deal with a classification problem that is a hybridization of fusion-based ensemble model with machine learning approaches, which produces a more trustworthy ensemble than the original ensemble model and outperforms previous heart disease prediction models. The proposed model is evaluated on the Cleveland heart disease dataset using six boosting techniques named XGBoost, AdaBoost, Gradient Boosting, LightGBM, CatBoost, and Histogram-Based Gradient Boosting. Hybridization produces superior results under consideration of classification algorithms. The remarkable accuracies of 96.51% for training and 93.37% for testing have been achieved by the Meta-XGBoost classifier.

Application of Artificial Neural Network Algorithms to Heart Disease Prediction Models with Python Programming

Heart disease is one of the deadliest diseases in and is the number one killer in the world so many studies are carried out to contribute to predicting a person's heart disease. This study aims to help create an early heart disease prediction model from the UCI Machine Learning Repository dataset. The method proposed in this study is a deep learning technique that applies an artificial neural network algorithm with a hidden layer technique in making a heart disease prediction model. This research stage found problems in improving the accuracy of the datasets used by dealing with problems in pre-processing data, such as missing data and determining the form of data correlation. The model was then tested through a heart disease dataset and yielded 90% accuracy. With the creation of this prediction model with python programming, it is hoped that in addition to helping to make disease predictions, it can also provide further innovations in data science in the health sector.

A design and implementation of heart disease prediction model using data and ECG signal through hybrid clustering

ABSTRACT Detection of heart disease has become a significant topic in the medicalindustry while analyzing clinical information. Over the past years,heart disease diagnosis has been one of the emerging techniques indata mining. Besides this, developing an automated prediction systemis a challenging task, even when detecting the disease by ECG signaland data. Here, it proposes a flexible method to detect heart diseasewith the domain of both signal processing and data mining. Initially,the acquired features from the weighted feature extraction approachare fed into the hybrid clustering model; in turn, the two differentclustering results are taken to determine the finest output. Thehybrid clustering model is developed by K-Means clustering that issuperimposed with DBCAN, where the centroid and value are optimizedwith the aid of Modified Updating-based Chicken Swarm Optimization(MU-CSO). Simultaneously, the ECG signals are garnered and decomposedusing Discrete Wavelet Transform (DWT). Due to the curse ofdimensionality, the Principal Component Analysis (PCA) is deployed.Consequently, the MU-CSO-assisted hybrid clustering is employed todiagnose heart disease by ECG signal. Therefore, the efficacy of theprediction model is validated with various metrics and comparedagainst conventional methods.

Heart Disease Prediction Model Using Varied Classifiers with Score-Level Fusion

This paper aims to introduce a novel heart disease prediction model. Originally, the input data is subjected for preprocessing, in which the data cleaning takes place. The features like statistical, higher order statistical features, and symmetrical uncertainty are extracted from the preprocessed data. Then, the selected features are subjected to the classification process with an ensemble model that combines the classifiers like deep belief network (DBN), random forest (RF), and neural network (NN). At last, the score level fusion is carried out to provide the final output. To make the classification more precise and accurate, it is intended to tune the weights of DBN more optimally. A new self-adaptive honey bee mating optimization (SAHBMO) algorithm is implemented in this work for this optimal tuning. Finally, the performance of the presented scheme is computed over the existing approaches in terms of different metrics.

A metaheuristic-enabled training system for ensemble classification technique for heart disease prediction

Various conditions that affect the muscles, blood arteries, heart, valves, or internal electrical pathways that regulate muscle contraction are referred to as "heart diseases." This work propose a new heart disease prediction model. The three main steps of the suggested framework are feature extraction, proposed dimensionality reduction, and proposed optimal ensemble-based heart disease prediction. Here, the most crucial components of the collected input data were extracted first. For accurate prediction, a significant number of features have been retrieved. Using Normalized Mutual Information induced Principal Component Analysis (NM-PCA), a novel feature dimension reduction technique has been proposed to overcome these problems. The dimensionally reduced features obtained by NM-PCA are simultaneously trained by the SVM, RF, and KNN classifiers. The improved optimized RNN generates the final prediction result. This optimized RNN is trained using the relevant outputs from SVM, RF, and KNN. Deer Updated Moth flame Optimization is used to adjust the weight function of the optimized RNN since it makes the ultimate judgment (DUMFO). After utilizing DUMFO, the RNN's prediction accuracy has greatly increased. The Deer Hunting Optimization Algorithm (DHO) and the Moth Flame Optimization (MFO) algorithms are combined to generate the DUMFO. Finally, using both positive and negative criteria, the performance of the suggested methodology is compared to existing, methodologies. The accuracy of the projected model is 47.3%, 45.2%, 57.8%, 36.8%, 26.3%, 14.7%, 16.8%, 5.12% and 3.15% superior to existing models like DBN, NN, CNN, LSTM, PM-LU, GA, SMO+EC, SSO+EC, BOA+EC, WOA+EC, MFO+EC and DHOA+EC, respectively.

A Heart Disease Prediction Model Based on Feature Optimization and Smote-Xgboost Algorithm

In today’s world, heart disease is the leading cause of death globally. Researchers have proposed various methods aimed at improving the accuracy and efficiency of the clinical diagnosis of heart disease. Auxiliary diagnostic systems based on machine learning are designed to learn and predict the disease status of patients from a large amount of pathological data. Practice has proved that such a system has the potential to save more lives. Therefore, this paper proposes a new framework for predicting heart disease using the smote-xgboost algorithm. First, we propose a feature selection method based on information gain, which aims to extract key features from the dataset and prevent model overfitting. Second, we use the Smote-Enn algorithm to process unbalanced data, and obtain sample data with roughly the same positive and negative categories. Finally, we test the prediction effect of Xgboost algorithm and five other baseline algorithms on sample data. The results show that our proposed method achieves the best performance in the five indicators of accuracy, precision, recall, F1-score and AUC, and the framework proposed in this paper has significant advantages in heart disease prediction.

Development and Validation of a Coronary Heart Disease Risk Prediction Model in Snorers with Hypertension: A Retrospective Observed Study.

To develop and validate a risk prediction model for coronary heart disease (CHD) in snorers with hypertension, including traditional and new risk factors. Twenty factors were evaluated in the records of 2810 snorers with hypertension. Training (70%) and validation (30%) sets were created by random allocation of data, and a new nomogram model was developed. The model's discrimination and calibration were measured by calculating the area under the receiver operating curve (AUC) and creating calibration charts. The performance of the nomogram model was compared with that of the Prediction for ASCVD Risk in China (China-PAR) and Framingham models by decision curve analysis. An optimal cutoff point for the risk score in the training set was computed to stratify patients. In the nomogram model, the AUCs for predicting CHD at 5, 7 and 9 years in the training set were 0.706 (95% confidence interval [CI] 0.649-0.763), 0.703 (95% CI 0.655-0.751) and 0.669 (95% CI 0.593-0.744), respectively. The respective AUCs were 0.682 (95% CI 0.607-0.758), 0.689 (95% CI 0.618-0.760) and 0.664 (95% CI 0.539-0.789) in the validation set. The calibration chart showed that the predicted events from the nomogram score were close to the observed events. Decision curve analysis indicated that the nomogram score was slightly better than the Prediction for ASCVD Risk in China (China-PAR) and Framingham models for predicting the risk of CHD in snorers with hypertension. A cutoff point was identified for being CHD-free (a nomogram score of ≤121), which could be helpful for the early identification of individuals at high-risk of CHD. The nomogram score predicts the risk probability of CHD in snorers with hypertension at 5, 7 and 9 years, and shows good capability in terms of discrimination and calibration. It may be a useful tool for identifying individuals at high risk of CHD.

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.

OPTIMIZATION ASSISTED HYBRID INTELLIGENT SYSTEM FOR HEART DISEASE PREDICTION

This research work proposes a new intelligent technique for predicting heart disease. The heart disease prediction model consists of three major processes like feature extraction, optimal feature selection, and classification. The input data are first fed into the feature extraction procedure, which extracts features based on central tendency, degree of dispersion, qualitative variation, Chi-squared feature, ReliefF, and symmetrical uncertainty (SU). However, the ’curse of dimensionality is the fundamental concern in this circumstance. Thus, it becomes essential to choose the optimal or best features to proceed with the disease prediction. To choose the right feature, this study uses a new hybrid algorithm called the Sea Lion Adopted Dragonfly Algorithm (SADA). Following that, the selected characteristics are fed into a hybrid classifier that combines the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN). The suggested approach optimizes the count of filter size in CNN to make the system more accurate in disease prediction (SADA). Finally, the performance of the proposed work is analyzed and verified using existing models for specific performance metrics.

Applying and Improving Accuracy of Heart Disease Prediction Model using Meta-classifiers and Ensemble Learning Methods with Feature Selection

Healthcare industry is a significant sector for producing an enormous amount of data daily. The lack of helpful information is the primary motive for introducing machine learning or data mining techniques for extracting the required pattern needed to make a decision. Globally, heart disease is the leading cause of death. Prediction of heart disease early may help the survival of the patient life. This paper explores the machine learning technologies, ensemble learning, and meta-classifier to predict heart disease with feature selection methods to improve the accuracy. It presents a performance comparison between classifiers, ensemble learning methods, and meta-classifier

Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features

Heart disease is a danger to people’s health because of its prevalence and high mortality risk. Predicting cardiac disease early using a few simple physical indications collected from a routine physical examination has become difficult. Clinically, it is critical and sensitive for the signs of heart disease for accurate forecasts and concrete steps for future diagnosis. The manual analysis and prediction of a massive volume of data are challenging and time-consuming. In this paper, a unique heart disease prediction model is proposed to predict heart disease correctly and rapidly using a variety of bodily signs. A heart disease prediction algorithm based on the analysis of the predictive models’ classification performance on combined datasets and the train-test split technique is presented. Finally, the proposed technique’s training results are compared with the previous works. For the Cleveland, Switzerland, Hungarian, and Long Beach VA heart disease datasets, accuracy, precision, recall, F1-score, and ROC-AUC curves are used as the performance indicators. The analytical outcomes for Random Forest Classifiers (RFC) of the combined heart disease datasets are F1-score 100%, accuracy 100%, precision 100%, recall 100%, and the ROC-AUC 100%. The Decision Tree Classifiers for pooled heart disease datasets are F1-score 100%, accuracy 98.80%, precision 98%, recall 99%, ROC-AUC 99%, and for RFC and Gradient Boosting Classifiers (GBC), the ROC-AUC gives 100% performance. The performances of the machine learning algorithms are improved by using five-fold cross validation. Again, the Stacking CV Classifier is also used to improve the performances of the individual machine learning algorithms by combining two and three techniques together. In this paper, several reduction methods are incorporated. It is found that the accuracy of the RFC classification algorithm is high. Moreover, the developed method is efficient and reliable for predicting heart disease.

An Ensemble Heart Disease Prediction Model Bagged with Logistic Regression, Naïve Bayes and K Nearest Neighbour.

Cardiovascular disease is an immense threat in today’s world. Every year lots of people died by this disease. Though medical science evolved and evaluated much extends still in many areas of the world there are lack of medical support. Early detection of cardiovascular can save lives where machine learning can help us. Advancement of artificial intelligence and machine learning are now playing an effective role in disease prediction and different health care applications. In this paper a heart disease prediction model has been proposed using ensemble learning. Single learning mechanism may not give a proper result so by combing multiple learners better performance from the model can be achieved. In this paper bagging with Logistic Regression, Gaussian Naïve Bayes and K Nearest Neighbour have applied.

Heart disease diagnosis and prediction based on hybrid machine learning model

Heart disease is responsible for a considerable percentage of deaths that occur all over the globe, and it has developed into a major health risk for many individuals. The detection of cardiovascular diseases such as heart attacks, myocardial infarction diseases, and others like these is a crucial problem that must be met by the regular clinical data analysis. Early prevalence of heart failure may save the lives of many people. The application of machine learning (ML) can provide an efficient answer to the problem of making decisions and accurate forecasts. The application of machine learning strategies is making significant headway in the medical sector. In the work that has been presented, a unique technique to machine learning is proposed for the purpose of predicting cardiac disease. The PhysioNet Dataset was utilised for the study that was proposed, and data mining algorithms like regression and classification were utilised. Support Vector Machine, Decision Tree and Random Forest are both the machine learning approaches that are utilised here. The cutting-edge strategy for the machine learning model has been devised. Support Vector Machine, Random Forest, Decision Tree, and the Hybrid model (Hybrid of SVM, RF and DT) are the four types of machine learning algorithms that are utilised in the implementation process. The accuracy level of the heart disease prediction model using the hybrid model was found to be 88.7 percent based on the results of the experiments. The user's input parameter will be utilised to predict heart illness, which will be done with a model that is a hybrid of Decision Tree and Random Forest. The interface is built to acquire the user's input parameter.

Prediction of Heart Disease Using Machine Learning Techniques

Heart disease is one of the significant causes of mortality in the world today. Predicting cardio vascular disease has become the critical challenge in the area of clinical data analysis. Machine learning (ML) is very effective in making decisions and predictions from the large quantity of data produced by the health care industry. Machine Learning techniques are used in recent developments in different areas of the Internet of Things (IoT). Various studies provides only a glimpse in predicting heart disease using Machine Learning techniques which aims to get the features by applying ML techniques that results higher accuracy in the prediction of heart disease. The heart disease prediction model is developed using various combinations of classification and feature techniques, which provides performance accuracy of 88.7% using hybrid random forest with linear model (HRFLM).

PREDICTION OF CARDIOVASCULAR DISEASE BY MACHINE LEARNING AND IOT

Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM). Based on the sensors connected with the human body the ML algorithm forecast the future health status.

EFFECTIVE HEART DISEASE PREDICTION USING HYBRID MACHINE LEARNING TECHNIQUES

Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM)

A Machine Learning Based Framework for Heart Disease Detection

Even in rural parts of many nations, coronary heart disease has emerged as the main cause of mortality. More than 23 million people will die from cardiovascular disease by 2030, according to the World Health Organization (WHO). With the use of cardiovascular disease prediction, healthcare practitioners may check the characteristics necessary for diagnosis, such as blood pressure and diabetes, which are vital. Many data mining methods are currently used in the medical industry, but additional research is needed to evaluate how well these categorization approaches function in real-world settings. The project's purpose is to quickly identify the best candidates for constructing heart disease prediction models, and to do it in a timely manner. The goal of this study is to increase the accuracy of cardiac disease prediction by addressing and overcoming the issues in the area (CVDs). CAD systems, which help physicians make choices, are often developed as a result of breakthroughs in machine learning technology. The categorization and prediction of cardiac disease are discussed in this article. The algorithms explored include ANN, KNN, and CNN. To conduct the evaluation, we utilised the UCI Cleveland database. For these algorithms, a thorough evaluation of the utility and consistency of data mining approaches found that CNNs performed best. Usefulness and consistency of data mining techniques for these algorithms revealed that the CNN was the most reliable process.

An automated clinical decision support system for predicting cardiovascular disease using ensemble learning approach

AbstractWith the vast advancements in the medical domain, earlier prediction of disease plays a substantial role in enhancing the healthcare quality and assists in taking better decisions making during emergency times. Most of the existing research concentrates on modeling an automated prediction model for heart disease and the risk factors. Nevertheless, accurate classification is a vital challenge in heart disease diagnosis where the managing of high‐dimensional data increases the execution time of existing classifiers. In this paper, a new ensemble model has been proposed with the aid of random subspace and K‐nearest neighbor (RSS‐KNN) scheme for earlier prediction of heart disease. Primarily, the proposed scheme implements an isolation‐based outlier removal mechanism to eradicate the noises and outliers in the distributed data. Subsequently, the essential features are identified using RSS by varying the testing and training errors in the evaluation phase. The extracted features are then fed into KNN for the accurate classification of heart disease. Finally, an enhanced squirrel optimizer has been employed in the proposed scheme to obtain the global results which balance the exploration as well as exploitation issues and eliminate the over‐fitting problems. The simulation results manifest that the accuracy (without features) of the proposed ensemble RSS‐KNN scheme in the UCI ML dataset is 97.65%, accuracy (with features) is 98.56%, and specificity is 98.10% when compared with existing state‐of‐the‐art classifiers.

An Integrated Machine Learning Model for Heart Disease Classification and Categorization

Progressions in the coordination among the machine learning algorithms, helped to achieve high accuracy and reliability in decision-making systems. Due to the impact and importance of heart diseases in real life, designing the efficient heart disease prediction model become a pivotal aspect today. Former research scholars applied the popular supervised machine learning models like Decision Trees, Naïve Bayes, ANN's, and FNN's to implement the heart disease prediction systems. As the heart disease prediction process is a multi-layered operation, each layer is expecting the optimal machine learning algorithm and the coordination among the algorithms of different layers to minimize the errors in prediction results. In this study, we proposed a new fuzzy neural-genetic algorithm to design an efficient and accurate heart disease prediction system. In our prediction system, we integrated the genetic algorithm, neural networks, and fuzzy logic technologies for training, classification, and categorization processes respectively. Experimental evaluations of Cleveland's heart disease dataset proved that the proposed fuzzy neural-genetic algorithm-based prediction system achieved high accuracy and low error rate when compared with the other machine learning models.