Heart Disease Prediction Model Research Articles

Remote Health Patient Monitoring System for Early Detection of Heart Disease

This paper presents a heart disease prediction model. Among the recent technology, internet of things-enabled healthcare plays a vital role. The medical sensors used in healthcare provide a huge volume of medical data in a continuous manner. The speed of data generation in IoT healthcare is high so the volume of data is also high. In order to overcome this problem, the proposed model is a novel three-step process to store and analyze the large volumes of data. The first step focuses on a collection of data from sensor devices. In Step 2, HBase has been used to store the large volume of medical sensor data from a wearable device to the cloud. Step 3 uses Mahout for devolving logistic regression-based prediction model. At last, ROC curve is used to find the parameters that cause heart disease.

Heart Disease Prediction Model Using Naïve Bayes Algorithm and Machine Learning Techniques

These days, heart disease comes to be one of the major health problems which have affected the lives of people in the whole world. Moreover, death due to heart disease is increasing day by day. So the heart disease prediction systems play an important role in the prevention of heart problems. Where these prediction systems assist doctors in making the right decision to diagnose heart disease easily. The existing prediction systems suffering from the high dimensionality problem of selected features that increase the prediction time and decrease the performance accuracy of the prediction due to many redundant or irrelevant features. Therefore, this paper aims to provide a solution of the dimensionality problem by proposing a new mixed model for heart disease prediction based on (Naïve Bayes method, and machine learning classifiers).In this study, we proposed a new heart disease prediction model (NB-SKDR) based on the Naïve Bayes algorithm (NB) and several machine learning techniques including Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest. This prediction model consists of three main phases which include: preprocessing, feature selection, and classification. The main objective of this proposed model is to improve the performance of the prediction system and finding the best subset of features. This proposed approach uses the Naïve Bayes technique based on the Bayes theorem to select the best subset of features for the next classification phase, also to handle the high dimensionality problem by avoiding unnecessary features and select only the important ones in an attempt to improve the efficiency and accuracy of classifiers. This method is able to reduce the number of features from 13 to 6 which are (age, gender, blood pressure, fasting blood sugar, cholesterol, exercise induce engine) by determining the dependency between a set of attributes. The dependent attributes are the attributes in which an attribute depends on the other attribute in deciding the value of the class attribute. The dependency between attributes is measured by the conditional probability, which can be easily computed by Bayes theorem. Moreover, in the classification phase, the proposed system uses different classification algorithms such as (DT Decision Tree, RF Random Forest, SVM Support Vector machine, KNN Nearest Neighbors) as a classifiers for predicting whether a patient has heart disease or not. The model is trained and evaluated using the Cleveland Heart Disease database, which contains 13 features and 303 samples.Different algorithms use different rules for producing different representations of knowledge. So, the selection of algorithms to build our model is based on their performance. In this work, we applied and compared several classification algorithms which are (DT, SVM, RF, and KNN) to identify the best-suited algorithm to achieve high accuracy in the prediction of heart disease. After combining the Naive Bayes method with each one of these previous classifiers the performance of these combines algorithms is evaluated by different performance metrics such as (Specificity, Sensitivity, and Accuracy). Where the experimental results show that out of these four classification models, the combination between the Naive Bayes feature selection approach and the SVM RBF classifier can predict heart disease with the highest accuracy of 98%. Finally, the proposed approach is compared with another two systems which developed based on two different approaches in the feature selection step. The first system, based on the Genetic Algorithm (GA) technique, and the second uses the Principal Component Analysis (PCA) technique. Consequently, the comparison proved that the Naive Bayes selection approach of the proposed system is better than the GA and PCA approach in terms of prediction accuracy.

A Hybrid Framework for Heart Disease Prediction Using Machine Learning Algorithms

Cardiovascular Diseases (CVDs) are the primary cause for the sudden death in the world today from the past few years the disease has emerged greatly as a most unpredictable problem, not only in India the whole planet facing the criticality. So, there is a desperate need of valid, accurate and practical solution or application to diagnose the CVD problems in time for mandatory treatment. Predicting the CVD is a great challenge in the health care domain of clinical data analysis. Machine learning Algorithms (MLA) and Techniques has been vastly developed and proven to be effective and efficient in predicting the problems using the past data. Using these MLA techniques and taking the clinical dataset which provided by the healthcare industry. Different studies were takes place and tried only a small part into predicting CVD with ML Algorithms. In this thesis, we propose the different novel methodology which concentrates at finding appropriate features by using MLA techniques resulting at finding out the accurate model to predict CVD. In this prediction model we are trying to implement the models with different combinations of features and several known classification techniques such as Deep Learning, Random Forest, Generalised Linear Model, Naïve Bayes, Logistic Regression, Decision Tree, Gradient Boosted trees, Support Vector Machine, Vote and HRFLM and we have got an higher accuracy level and of 75.8%, 85.1%, 82.9%, 87.4%, 85%, 86.1%, 78.3%, 86.1%, 87.41%, and 88.4% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

Early prediction model for coronary heart disease using genetic algorithms, hyper-parameter optimization and machine learning techniques

Coronary Heart Disease (CHD) is one of the major causes of morbidity and mortality worldwide. According to the World Health Organization (WHO) survey, Cardiac arrest accounts for more deaths annually than any other cause. But the silver lining over here is that heart related diseases are highly preventable, if simple lifestyle modifications are carried out. However, it is a challenging factor to identify high risk heart patients at times due to other comorbidity factors such as diabetes, high blood pressure, high cholesterol and so on. Hence it is needed to develop an efficient early prediction model which can detect high risk patients and their life could be saved. The proposed system helps to identify the best set of features for diagnosis using traditional machine learning algorithms along with modern Gradient Boosting approaches. Genetic algorithm for feature selection to optimize performance by reducing the number of parameters by 20% whilst keeping the accuracy of the model intact is implemented in the proposed system. In addition, hyper parameter optimization techniques are executed to further improve the predictive model’s performance.

Weight optimized neural network for heart disease prediction using hybrid lion plus particle swarm algorithm.

Heart disease remains one of the significantcauses ofmortality and morbidity amongst the world's population. Predicting heart disease is considered as one of the vital issues in clinical data analysis. Since the number of data is rising gradually, it is muchcomplicatedforanalyzing and processing, and especially, it becomes difficult to maintain the e-healthcare data. Moreover, the prediction model under machine learning seems to be anessentialfacet in this research area. In this scenario, this paper aims to propose a new heart disease prediction model with the inclusion of specificprocesses like Feature Extraction, Record, Attribute minimization, and Classification. Initially, both statistical and higher-order statistical features are extracted under feature extraction. Subsequently, the record and attribute minimization carried out, where Component Analysis PCA plays its major role in solving the "curse of dimensionality."Finally, the prediction process takes place by the Neural Network (NN) model that intake the dimensionally reduced features. Moreover, the major intention of this paper deals with the accurate prediction. Hence, it is planned to influence the utility of meta-heuristic algorithms for the weight optimization of NN. This paper introduces a new hybrid algorithm termed Particle Swarm Optimization (PSO) merged LA update (PM-LU) algorithm that solves the above-mentioned optimization crisis, which hybrids the concept of Lion Algorithm (LA) and PSO algorithm. Finally, the efficiency of proposed work is compared over other conventional approaches and its superiority is proven with respect to certain performance measures. From the analysis, the presented PM-LU-NN scheme with regards to accuracy is 3.85%, 12.5%, 12.5%, 3.85%, and 7.41% better than LM-NN, WOA-NN, FF-NN, PSO-NN and LA-NN algorithms.

Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation.

BackgroundPredictions of cardiovascular disease risks based on health records have long attracted broad research interests. Despite extensive efforts, the prediction accuracy has remained unsatisfactory. This raises the question as to whether the data insufficiency, statistical and machine-learning methods, or intrinsic noise have hindered the performance of previous approaches, and how these issues can be alleviated.ObjectiveBased on a large population of patients with hypertension in Shenzhen, China, we aimed to establish a high-precision coronary heart disease (CHD) prediction model through big data and machine-learningMethodsData from a large cohort of 42,676 patients with hypertension, including 20,156 patients with CHD onset, were investigated from electronic health records (EHRs) 1-3 years prior to CHD onset (for CHD-positive cases) or during a disease-free follow-up period of more than 3 years (for CHD-negative cases). The population was divided evenly into independent training and test datasets. Various machine-learning methods were adopted on the training set to achieve high-accuracy prediction models and the results were compared with traditional statistical methods and well-known risk scales. Comparison analyses were performed to investigate the effects of training sample size, factor sets, and modeling approaches on the prediction performance.ResultsAn ensemble method, XGBoost, achieved high accuracy in predicting 3-year CHD onset for the independent test dataset with an area under the receiver operating characteristic curve (AUC) value of 0.943. Comparison analysis showed that nonlinear models (K-nearest neighbor AUC 0.908, random forest AUC 0.938) outperform linear models (logistic regression AUC 0.865) on the same datasets, and machine-learning methods significantly surpassed traditional risk scales or fixed models (eg, Framingham cardiovascular disease risk models). Further analyses revealed that using time-dependent features obtained from multiple records, including both statistical variables and changing-trend variables, helped to improve the performance compared to using only static features. Subpopulation analysis showed that the impact of feature design had a more significant effect on model accuracy than the population size. Marginal effect analysis showed that both traditional and EHR factors exhibited highly nonlinear characteristics with respect to the risk scores.ConclusionsWe demonstrated that accurate risk prediction of CHD from EHRs is possible given a sufficiently large population of training data. Sophisticated machine-learning methods played an important role in tackling the heterogeneity and nonlinear nature of disease prediction. Moreover, accumulated EHR data over multiple time points provided additional features that were valuable for risk prediction. Our study highlights the importance of accumulating big data from EHRs for accurate disease predictions.

RETRACTED ARTICLE: An optimal artificial neural network based big data application for heart disease diagnosis and classification model

At present days, world is facing several issues like irregular distribution of medicinal resources, new chronic diseases, and the raising operating cost. The way of combining recent technologies into the medical system will helps to significantly resolve the problems. This study introduces a big health application system based on optimal artificial neural network (OANN) for heart disease diagnosis, which is considered as a deadliest disease in all over the globe. The proposed OANN includes a set of two main processes namely, distance based misclassified instance removal (DBMIR) and teaching and learning based optimization (TLBO) algorithm for ANN, called (TLBO-ANN). The proposed model is developed using a Big Data framework like Apache Spark. The presented OANN model operates on two phases, namely offline prediction and online prediction. During the offline prediction stage, the benchmark heart disease dataset will be used to train a model and performs testing. Similarly, at the online prediction stage, the real time data will be streamed into Apache Spark model and the filtered data will be diagnosed by the use of trained model to obtain the prediction results. The performance of the presented OANN model has been tested using a benchmark heart disease dataset from UCI repository. A comprehensive experimental result analysis clearly verified the better outcome of the OANN model over the compared methods. The proposed method is found to be an effective tool to analyze big data based heart disease prediction model to satisfy the need of increasing number of heart patients.

RETRACTED ARTICLE: A hybrid approach for mortality prediction for heart patients using ACO-HKNN

Heart disease is the major cause of mortality in the world. The heart disease prediction from the clinical data is deliberate as the most important subject in clinical data analysis. Especially the size of data in health care is vast. Data mining (DM) assists decision and prediction from the raw health care data. DM converts the large collection into useful information. Several existing studies utilize the data mining approaches in heart disease prediction. There is only little research focused on selecting the important features which play a significant role in predicting heart disease is less. The aim of this study is to provide an enhanced approach with novel feature selection and classification technique to predict mortality in congestive heart failure patients. Through this approach the death rate due to heart disease will be decreased gradually. The ant colony optimization (ACO) algorithm is utilized for selecting the best feature for hybrid K-nearest neighbor (KNN) classifier. The proposed approach is compared with the prior classification techniques such as the Support vector machine, Naive Bayes, KNN, C4.5, and decision tree. UCI Cleveland dataset is utilized for our implementation. Using the Netbeans IDE an experimental was conducted and the result shows that the heart disease prediction model provides a better result with accuracy of 99.2%.The present study shows the efficiency of the HKNN in heart disease prediction system. Initially important features are analyzed and then classification is utilized to obtain a better result.

Effective Cardiovascular Disease Prediction using Hybrid Machine Learning Techniques

In today’s era deaths due to heart disease has become a major issue approximately one person dies per minute due to heart disease. This is considering both male and female category and this ratio may vary according to the region also this ratio is considered for the people of different age groups.Heart disease is one of the most fatal problems in the whole world, which cannot be seen with a naked eye and comes instantly when its limitations are reached.There are various data mining and machine learning techniques and tools available to extract effective knowledge from databases and to use this knowledge for more accurate diagnosis and decision making.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. We produce an enhanced performance level with an accuracy through the prediction model for heart disease with the proposed method hybrid random forest with a linear model.In this paper commonly used machine learning techniques and their complexities are summarized.

HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System

Heart disease, one of the major causes of mortality worldwide, can be mitigated by early heart disease diagnosis. A clinical decision support system (CDSS) can be used to diagnose the subjects' heart disease status earlier. This study proposes an effective heart disease prediction model (HDPM) for a CDSS which consists of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and eliminate the outliers, a hybrid Synthetic Minority Over-sampling Technique-Edited Nearest Neighbor (SMOTE-ENN) to balance the training data distribution and XGBoost to predict heart disease. Two publicly available datasets (Statlog and Cleveland) were used to build the model and compare the results with those of other models (naive bayes (NB), logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF)) and of previous study results. The results revealed that the proposed model outperformed other models and previous study results by achieving accuracies of 95.90% and 98.40% for Statlog and Cleveland datasets, respectively. In addition, we designed and developed the prototype of the Heart Disease CDSS (HDCDSS) to help doctors/clinicians diagnose the patients'/subjects' heart disease status based on their current condition. Therefore, early treatment could be conducted to prevent the deaths caused by late heart disease diagnosis.

Recursion Enhanced Random Forest With an Improved Linear Model (RERF-ILM) for Heart Disease Detection on the Internet of Medical Things Platform

Nowadays, Heart disease is one of the crucial impacts of mortality in the country. In clinical data analysis, predicting cardiovascular disease is a primary challenge. Deep learning (DL) has been demonstrated to be effective in helping to determine and forecast a huge amount of data produced by the health industry. In this paper, the proposed Recursion enhanced random forest with an improved linear model (RFRF-ILM) to detect heart disease. This paper aims to find the key features of the prediction of cardiovascular diseases through the use of machine learning techniques. The prediction model is adding various combinations of features and various established methods of classification. it produces a better level of performance with precision through the heart disease prediction model. In this study, the factors leading to cardiovascular disease can be diagnosed. A comparison of important variables showed with the Internet of Medical Things (IoMT) platform, for data analysis. This indicates that coronary artery disease develops more often in older ages. Also important in this disease's outbreak is high blood pressure. For this purpose, measures must be taken to prevent this disease and Diabetes provides a further aspect that should be taken into consideration in the occurrence of coronary artery disease with 96.6 % accuracy,96.8% stability ratio and 96.7% F-measure ratio.

Heart Disease Prediction Model based Ongradient Boosting Tree (GBT) Classification Algorithm

Recently, Heart disease (HD) is the main cause of increasing death rate all over the world. Data classification is a crucial task in the medical field which assists the physicians to predict the diseases. Recently, machine learning (ML) algorithms have been employed to classify the data in the medical field. The data complexity and quantity needs to be examined and managed to transform the efficient and accurate HD diagnosis. In this paper, a gradient boosting tree (GBT) based classifier or gradient boosting classifier (GBC) model to predict the HD efficiently. Besides, a set of extensive experiments were carried out using Staglog and Cleveland heart disease dataset. The experimental values ensured the superiority of the GBT classifier based on several performance measures.

The methods of duo output neural network ensemble for prediction of coronary heart disease

The occurrence of Coronary heart disease (CHD) is hard to predict yet , but the assessment of CHD risk for the next ten years is possible . The p rediction of coronary heart disease can be modelled using multi-layer perceptron neural network (MLP-ANN). Prediction model with MLP-ANN ha s either positive or negative CHD output , which is a binary classification. A prediction model with binary classification requires determin ation of threshold value before the classification process which increases the uncertainty in the classification process. Another weakness of the MLP-ANN model is the presence of overfitting. This study propose s a prediction model for coronary heart disease using the duo output artificial neural network ensemble (DOANNE) method to overcome the problems of overfitting and uncertainty of classification in MLP-ANN. This research method wa s divided into several stages, namely data acquisition, pre-processing, modelling into DOANNE, neural network ensemble training with Levenberg-Marquard (LM) algorithm, system performance testing, and evaluation. The results of the study showed that the use of DOANNE-LM method was able to provide a significant improvement from the MLP-ANN method, indicated by the results of statistical tests with p-value <0.05.

The methods of duo output neural network ensemble for prediction of coronary heart disease

The occurrence of Coronary heart disease (CHD) is hard to predict yet , but the assessment of CHD risk for the next ten years is possible . The p rediction of coronary heart disease can be modelled using multi-layer perceptron neural network (MLP-ANN). Prediction model with MLP-ANN ha s either positive or negative CHD output , which is a binary classification. A prediction model with binary classification requires determin ation of threshold value before the classification process which increases the uncertainty in the classification process. Another weakness of the MLP-ANN model is the presence of overfitting. This study propose s a prediction model for coronary heart disease using the duo output artificial neural network ensemble (DOANNE) method to overcome the problems of overfitting and uncertainty of classification in MLP-ANN. This research method wa s divided into several stages, namely data acquisition, pre-processing, modelling into DOANNE, neural network ensemble training with Levenberg-Marquard (LM) algorithm, system performance testing, and evaluation. The results of the study showed that the use of DOANNE-LM method was able to provide a significant improvement from the MLP-ANN method, indicated by the results of statistical tests with p-value <0.05.

An IoT based efficient hybrid recommender system for cardiovascular disease

A fog-based IoT model can be helpful for patients from remote areas with cardiovascular disease. An expert cardiologist is usually not available in such remote areas. There are some systems available to classify heart disease and provide recommendations but these existing systems only use classification for recommendations. From this line of research, we propose an IoT based efficient community-based recommender system that diagnoses cardiac disease and its type and provides recommendations related to the physical and dietary plan. The first part intent to collect the data from the patient remotely by using the bio sensors. The IoT based environment is used to transmit the data to the server. Afterward, heart disease prediction model is implemented, that can diagnose the cardiovascular disease and classify into eight available cardiovascular classes i.e. Myocardial Infarction (MI stable), Myocardial Infarction (MI unstable), Acute Coronary Syndrome (ACS), Atrial Fibrillation (AF), Hypertension (HTN), Ischemic Heart Disease (IHD), Left Ventricular Hypertrophy (LVH), Chronic Heart Failure/ Left Ventricle Function (CCF/LVF), Supraventricular Tachycardia (SVT). The second part pursues to provide physical and dietary plan recommendation to the cardiac patient according to gender and age groups. A dataset for diseases and corresponding recommendations is collected from a well-renowned hospital with the help of an expert cardiologist. The performance of the system is evaluated in terms of precision, recall and Mean absolute error and achieves 98% accuracy.

Enhancing Heart Disease Prediction Models with Feature Selection and Ensemble Methods

Enhancing Heart Disease Prediction Models with Feature Selection and Ensemble Methods

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).

Heart Disease Prediction based on External Factors: A Machine Learning Approach

Technology has immensely changed the world over the last decade. As a consequence, the life of the people is undergoing multiple changes that directly have positive and negative effects on health. Less physical activity and a lot of virtual involvements are pushing people into various health-related issues and heart disease is one of them. Currently, it has gained a great deal of attention among various life-threatening diseases. Heart disease can be detected or diagnosed by different medical tests by considering various internal factors. However, this type of approach is not only time-consuming but also expensive. Concurrently, there are very few studies conducted on heart disease prediction based on external factors. To bridge this gap, we proposed a heart disease prediction model based on the machine learning approach which enables predicting heart disease with 95% accuracy. To acquire the best result, 6 distinct machine learning classifiers (Decision Tree, Random Forest, Naive Bayes, Support Vector Machine, Quadratic Discriminant, and Logistic Regression) were used. At the same time, sklearn.ensemble.ExtraTreesClassifier has been used to extract relevant features to improve predictive accuracy and control over-fitting. Findings reveal that Support Vector Machine (SVM) outperforms the others with greater accuracy (95%).

Identification of significant features and data mining techniques in predicting heart disease

Cardiovascular disease is one of the biggest cause for morbidity and mortality among the population of the world. Prediction of cardiovascular disease is regarded as one of the most important subject in the section of clinical data analysis. The amount of data in the healthcare industry is huge. Data mining turns the large collection of raw healthcare data into information that can help to make informed decision and prediction. There are some existing studies that applied data mining techniques in heart disease prediction. Nonetheless, studies that have given attention towards the significant features that play a vital role in predicting cardiovascular disease are limited. It is crucial to select the correct combination of significant features that can improve the performance of the prediction models. This research aims to identify significant features and data mining techniques that can improve the accuracy of predicting cardiovascular disease. Prediction models were developed using different combination of features, and seven classification techniques: k-NN, Decision Tree, Naive Bayes, Logistic Regression (LR), Support Vector Machine (SVM), Neural Network and Vote (a hybrid technique with Naïve Bayes and Logistic Regression). Experiment results show that the heart disease prediction model developed using the identified significant features and the best-performing data mining technique (i.e. Vote) achieves an accuracy of 87.4% in heart disease prediction.

Comparing the Consistency and Performance of Various Coronary Heart Disease Prediction Models for Primary Prevention Using a National Representative Cohort in Taiwan.

Predicting future coronary artery disease (CAD) risk by model-based approaches can facilitate identification of high-risk individuals for prevention and management. Therefore, we compared the consistency and performance of various CAD models for primary prevention using 1 external validation dataset from a national representative cohort in Taiwan.Methods and Results:The 10 CAD prediction models were assessed in a validation cohort of 3559 participants (≥35 years old, 53.5% women) from a Taiwanese national representative cohort that was followed up for a median 9.70 (interquartile range, 9.63-9.74) years; 63 cases were documented as developing CAD events. The overall κ value was 0.51 for all 10 models, with a higher value for women than for men (0.53 for women, 0.40 for men). In addition, the areas under the receiver operating characteristics curves ranged from 0.804 (95% confidence interval, 0.758-0.851) to 0.847 (95% confidence interval, 0.805-0.889). All non-significant chi-square values indicated good calibration ability. Our study demonstrated these 10 CAD prediction models for primary prevention were feasible and validated for use in Taiwanese subjects. Further studies of screening and management are warranted.