Early Detection Of Heart Disease Research Articles

Retracted: An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease.

[This retracts the article DOI: 10.1155/2022/3372296.].

Cardiac disease prediction using AI algorithms with SelectKBest.

Atherosclerotic cardiovascular disease (ASCVD), which includes coronary heart disease (CHD) and ischemic stroke, is the leading cause of mortality globally. According to the European Society of Cardiology (ESC), 26 million people worldwide have heart disease, with 3.6 million diagnosed each year. Early detection of heart disease will aid in lowering the mortality rate. The lack of diversity in training data and the difficulty in comprehending the findings of complicated AI models are the key issues in current research for heart disease prediction using artificial intelligence. To overcome this, in this paper, cardiac disease prediction using AI algorithms with SelectKBest has been proposed. Features are standardized, balanced, and selected using the StandardScaler, SMOTE, and SelectKBest techniques. Machine learning models such as support vector machine (SVM), K-nearest neighbor(KNN), decision tree (DT), logistic regression (LR), adaptive boosting (AB), naive Bayes (NB), random forest (RF), and extra tree (ET) and deep learning models such as vanilla long short-term memory (LSTM), bidirectional long short-term memory (LSTM), stacked long short-term memory (LSTM), and deep neural network (DNN) are assessed using Alizadeh Sani, combined (Cleveland, Hungarian, Switzerland, Long Beach VA, and Stalog), and Pakistan heart failure datasets. As a result of the evaluation, the proposed deep neural network (DNN) with SelectKBest predicted heart disease in a promising way. The prediction rate of unweighted accuracy of 99% on Alizadeh Sani, 98% on combined, and 97% on Pakistan are gained in tenfold cross-validation experiments. The suggested approach can be utilized to diagnose heart disease in its early stages.

Machine Learning-based Prognosis for Early Detection of Heart Disease – A Comparative Study

Medical analysis is occasionally cited as a valuable source of insightful data. Coronary heart disease (CHD), a common and serious complication of diabetes mellitus type 2 (T2DM), is one of the most common chronic conditions characterized by an imbalance in insulin secretion. T2DM commonly has poor outcomes and even fatalities as a result of these complications. Identification of those who have an a higher risk of CHD problems is becoming more and more important due to the enormous number of people with T2DM, but a predictive method is still lacking. Early detection of CHD can help reduce mortality rates because it is one of the main causes of death in the globe. The challenge arises from the complexity of the data and relationship prediction using conventional methods. The purpose of this project is to use machine learning (ML) technologies and historical medical data to predict CHD. This study's primary objective is to identify correlations in CHD data using supervised learning methods such as Support Vector Machines (SVM), Decision Trees and Ensemble Classifiers. The researched ML techniques produce intelligent models. Empirical results demonstrate that probabilistic approaches are promising in diagnosing CHD using a variety of performance assessment parameters. Key Words: heart, health, machine learning, ensemble

Pelatihan pembuatan sensor medis berbasi IoT sebagai pengenalan smart medical devices

Cardiovascular disease (CVD) is a leading cause of death globally, resulting in approximately 17.9 million deaths each year (WHO, 2017), with estimates projecting a rise to 23.3 million deaths by 2030 (Pusdatin Kemenkes RI, 2014). Early detection of heart disease plays a crucial role in CVD prevention, with heart rate (bpm) being a key indicator to assess heart function, ranging from 60 to 100 beats per minute. To address the need for early detection, a practical heart rate monitoring device utilizing the Internet of Things (IoT) and Smart Medical Devices (SMDs) was developed. This research aimed to provide training on IoT-based heart rate detection to high school students in Trenggalek. The training encompassed lectures and hands-on practice, successfully enhancing participants' knowledge of IoT, as demonstrated by improved test scores. Moreover, the training resulted in a prototype of an IoT-based heart rate monitoring system that utilizes Arduino and a heart rate sensor. Post-training evaluations showed the majority of participants were satisfied with the quality of materials and organization, indicating the positive impact of this engagement on the partners. The results support the potential of this IoT training to equip high school students with essential skills, fostering self-reliance in medical device production and reducing dependence on imports in the face of ASEAN Economic Community challenges. Ultimately, this initiative contributes to building a competent healthcare workforce in Indonesia

Klasifikasi MIT-BIH Arrhythmia Database Metode Random Forest dan CNN dengan Model ResNet-50: A Systematic Literature Review

Although Machine Learning and Deep Learning technologies have been widely used and have shown high accuracy in many applications, including in the health field, their application in early detection of heart disease still has room for improvement. Further research is needed to enhance the accuracy and efficiency of this process. This study aims to understand and improve the process of ECG signal extraction and classification based on Machine Learning and Deep Learning. Essentially, this research aims to evaluate and compare various models, focusing on the Random Forest and Convolutional Neural Networks (CNN) models. The study reviews several related researches, especially those focusing on the process of extraction and classification of ECG signals using Machine Learning and Deep Learning. After extraction and classification of data, an evaluation and comparison process is conducted to determine the best performing model. From the research conducted, it was found that Machine Learning methods generally show an accuracy rate between 97.02% - 99.66%, with the Random Forest method having an accuracy of 97.02%. Meanwhile, the CNN method shows a higher accuracy rate, which is between 98.75% - 100%. Thus, this research confirms the superiority of CNN in this classification process, and shows potential for further use in early detection of heart disease.

Machine Learning for Cardiovascular Disease Risk Assessment: A Systematic Review

Accurate diagnosis and early detection of heart disease can help save lives because it is the primary cause of mortality. If a forecast is inaccurate, patients could potentially suffer significant harm. Today, it is challenging to predict and identify heart disease. 24 hour monitoring is not practical due to the extensive equipment and time required. Heart disease treatments can be both expensive and challenging. In order to obtain the data from databases and use this information to successfully forecast cardiac illness, a variety of data mining techniques and machine learning algorithms are now accessible. We have used every technique to put the heart disease prognosis into practise. The algorithms used in SVM, NAIVE BAYER, REGRESSION, KNN, ADABOOST, DECISION TREE, and XG-BOOST And Voting Ensemble Method.

Machine Learning for Personalized Risk Assessment and Early Detection of Heart Disease

Abstract: Today in our busy life it very difficult for us to look after our health. Cardio vascular diseases are very common now days which cause loss of billions of lives world-wide. Our lifestyles have a major impact on our health causing various chronic diseases. Machine learning (ML) can revolutionize the field of cardiovascular disease prediction by providing more accurate, nuanced, and personalized risk assessment, leading to improved patient health. A properly trained machine learning model can easily detect or predict the heart disease. Within the context of using artificial intelligence for forecasting and assessing the occurrence of heart ailments, here are various factors that contribute to the heart disease to be considered, such as lifestyle choices and medical conditions to predict whether the person is at high risk or not. ML technology can also analyze large datasets of patient data to identify patterns and risk factors that cannot be easily distinguished, predicted, or detected using traditional methods. Estimators like k-NN classifier, Decision tree classifier, Gradient boosting classifier and Gaussian Naive bayes (NB) classifier, are provided with different characteristics or feature extracted from the dataset, demonstrated consistent and reliable performance in predicting heart disease. The utilization of these attributes can potentially aid the medical field for on time detection and diagnosis of the heart disease.

Comparative Study to Identify the Heart Disease Using Machine Learning Algorithms

Abstract: Heart disease is a major health concern globally, and it is a leading cause of death, especially in developing countries in Africa and Asia. Early detection of heart disease can play a crucial role in preventing its occurrence and reducing its impact. This is where the use of deep neural networks (CNNs) and the Django framework can be highly beneficial. The CNN algorithm is a machine learning technique that uses multiple layers of artificial neural networks to analyze complex data and identify patterns. In the case of heart disease prediction, the CNN algorithm can be trained on a dataset such as the UCI dataset, which contains a large amount of data related to heart disease risk factors and patient characteristics. The dataset can be split into training and testing data to enable the algorithm to learn and validate its accuracy.

A novel machine learning model with Stacking Ensemble Learner for predicting emergency readmission of heart-disease patients

Early detection of heart complications is highly effective in treating patients with cardiovascular diseases. Various machine learning methods have previously been used for the early detection of heart diseases. However, existing data-driven machine learning (ML) approaches fall short of providing efficient and accurate heart disease detection. This misdiagnosis causes significant overcrowding in medical care facilities by patients that do not need emergency readmission or fatalities caused by discharging patients requiring emergency. This study proposes a novel model for detecting emergency readmission of heart disease patients by effectively identifying patients who require emergency assistance before the onset of heart attacks or other heart-related complications. A robust Stacking Ensemble Learner (SEL) is developed using ensemble learning to maximize the detection performance. Our SEL method predicts whether a patient with heart problems is required to get admitted as an emergency case after a preliminary admission. To ensure robustness and high accuracy in the prediction results across multiple runs, the XGBoost is used as a meta-learner in the SEL model. The novelty of this paper lies in (1) the use of behavior-based features to create a new class label for emergency readmission, which has not been previously explored in the existing data-driven machine learning approaches, (2) the paper utilizes a comprehensive private dataset from the MIT Laboratory for Computational Physiology, not adopted in clinical studies on heart failure and cardiovascular disease, and (3) The development of a robust Stacking Ensemble Learner (SEL) using ensemble learning, with XGBoost as a meta-learner, also contributes to the novelty of this study, as it achieves higher prediction performance compared to the baseline models, the use of ensemble learning in the SEL model helps to overcome the limitations of unstable training of the individual classification models. Experimental results show that the stacking model provides high accuracy, Recall, and F1 score compared to the baseline models such as logistic regression, k-nearest neighbor, Decision tree, Random Forest, support vector machines, bagging, and boosting. The SEL model has achieved an accuracy of 88% in predicting emergency readmission of heart-disease patients, which is very promising for the production-ready model in clinical practice.

Early Detection of Heart Disease Using Machine Learning Approach

Heart attack is one of the leading causes of morbidity in the worldwide population. Cardiovascular disease is one of the major diseases involved in clinical data analysis or one of the most important part for forecasting. Early detection of cardiovascular diseases can help to reduce high-risk condition for heart patients to make individual decisions for their lifestyle adjustments, mitigating the challenges. Early detection of heart disease has been explored in this study using a machine-learning approach. Additionally, we used sampling strategies to deal with disparate datasets. The overall risk is estimated using a variety of machine-learning techniques. On Kaggle, the Heart Disease dataset is accessible and open for all. In present study testing set used this dataset. The ultimate objective is to determine whether the patient has a “10-year risk of developing coronary heart disease” (CHD). The dataset contained thirteen features that provided patient data, and the authors used machine learning algorithms to diagnose cardiac problems with 98.8% accuracy.

Abstract P530: Building a Heart Disease Detection Web Application

Introduction: Heart disease continues to be a leading cause of death worldwide; hence early detection of the disease is recommended to reduce the fatality rate. Consequently, prediction models with high accuracy scores become important. Previous research has demonstrated encouraging results; however, concerning issues such as variations in databases and small-seized databases are still present. Hypothesis: We assessed the hypothesis that the web application which includes user input, a large database, and a machine learning model, will be able to predict the occurrence of heart disease with a high accuracy score. Methods: A web application composed of two modules, the admin and the user side, was built. The admin side consisted of a prediction model that was built based on a database and a classification algorithm. When creating the admin side, this project used data from the third visit of the multi-center population-based cohort study, the Atherosclerosis Risk in Communities (ARIC) study. There were a total of 11,351 participants on the third ARIC visit. Heart disease and other variables such as age, gender, race, education, smoking, drinking, physical activity, etc., were selected to build the prediction model of the web application. The database was split into independent ‘x’s and dependent ‘y’, and was furthermore randomly split into 75% (8,514 of 11,351) training and 25% (2,837 of 11,351) testing data. A model was created through the random forest classifier based on the trained and tested data. The user side consisted of provided fields or features that allow the user to insert his input into the web application. The user input is compared with the data used to build the web application’s prediction model. After the user inputs his information, the prediction model predicts the disease and gives the model test accuracy scores. The web application was built in Python with the help of the main packages and libraries such as streamlit, pandas, numpy, sklearn, and PIL. Results: The machine learning model achieved high accuracy in predicting heart disease. Conclusions: In conclusion, the current findings suggest that high disease prediction model test accuracy scores can be achieved when using random forest. Machine learning can be an effective tool in the early detection of heart disease and in making decisions for further prevention and treatment improvement.

Heart disease classification using optimized Machine learning algorithms

Early detection of heart disease is exceptionally critical to saving the lives of human beings. Heart attack is one of the primary causes of high death rates throughout the world, due to the lack of human and logistical resources in addition to the high costs of diagnosing heart diseases which plays a key role in the healthcare sector, this model is suggested. In the field of cardiology, patient data plays an essential role in the healthcare system. This paper presents a proposed model that aims to identify the optimal machine learning algorithm that can predict heart attacks with high accuracy in the early stages. The concepts of machine learning are used for training and testing the model based on the patient's data for effective decision-making. The proposed model consists of three stages, the first stage is patient data collection and processing, and the second stage is data training and testing using machine learning algorithms Random Forest, Support Vector Machines, K-Nearest Neighbor, and Decision Tree) that show The best classification (94.958 percent) with the Random Forest algorithm and the third stage is optimized the classification results using one of the hyperparameters optimization techniques random search that shows The best accuracy was (95.4 percent) obtained also with RF

Adaptive Features Selection Technique for Efficient Heart Disease Prediction

Heart disease is a common disease that causes death and is difficult to detect manually. A more efficient classification model that relies on machine learning methods to achieve higher classification accuracy, attracts the attention of researchers to design an effective prediction model. Moreover, it plays an important role in the practical application of medical cardiology with the aim of early detection of heart diseases. In this paper, an efficient and accurate heart disease detection system is proposed based on the proposed adaptive feature selection technique using four machine learning methods: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). Two feature selection methods were used to design the proposed technique, mutual information (MI) and recursive feature elimination (RFE) to determine the optimal number of selected features that increase the performance of the classification models and reduce the time complexity of model implementation. The proposed technique was implemented on the two standard databases from the UCI machine learning repository: Cleveland heart disease and heart Statlog Cleveland. The best model was selected and saved as a prediction model using the cross-validation method. The results show that each data has a different number of features chosen according to the classifier model. For the first heart disease dataset, the best heart disease detection system Support Vector Machine-mutual information (SVM-MI) achieved the highest classification accuracy of approximately 96.755 compared to the other classifier models used. While the Random Forest-mutual information (RF-MI) model achieved an accuracy of 97.4% for the second data set. The proposed technique produced the highest prediction performance in terms of accuracy, f1 score, accuracy, and metric retrieval compared to the latest research in this field.

Digital Stethoscope for Early Detection of Heart Disease on Phonocardiography Data

Digital Stethoscope for Early Detection of Heart Disease on Phonocardiography Data

Design and Development of Cloud Based Real Time ECG Anomaly Detection

The functionality of the heart is assessed by analysing the electrocardiogram (ECG) signal. Numerous methods were developed for the accurate detection of heart diseases utilising the recorded ECG signals. However, individuals with serious heart diseases must be constantly observed, especially if there are few indicating bio-signal patterns. Real-time data collection and processing enables early detection of heart diseases and prevent complications. The AD8232 sensor is used in the project along with its interface with the Node MCU for real time acquisition of ECG signal. The intervals of the PQRST wave are thoroughly investigated utilizing a novel windowing approach based on Wavelet Transform. To increase the QoS in the healthcare system, the final findings are displayed on both the system and the cloud interface. In this work, we compared the ECG parameters with those from well-known smartwatches. The outcomes demonstrated a striking match between the ECG parameters acquired using our method and those obtained using smartwatches. This verifies the precision and dependability of our system in monitoring the ECG parameters from ECG signal. Our methodology has the potential to be a dependable and practical solution for heart rate monitoring in real-time and distant healthcare applications because our algorithm and smartwatches consistently report heart rate rates in the same range.

1D CNN model for ECG diagnosis based on several classifiers

One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases.

A Deep Convolutional Neural Network for the Early Detection of Heart Disease

Heart disease is one of the key contributors to human death. Each year, several people die due to this disease. According to the WHO, 17.9 million people die each year due to heart disease. With the various technologies and techniques developed for heart-disease detection, the use of image classification can further improve the results. Image classification is a significant matter of concern in modern times. It is one of the most basic jobs in pattern identification and computer vision, and refers to assigning one or more labels to images. Pattern identification from images has become easier by using machine learning, and deep learning has rendered it more precise than traditional image classification methods. This study aims to use a deep-learning approach using image classification for heart-disease detection. A deep convolutional neural network (DCNN) is currently the most popular classification technique for image recognition. The proposed model is evaluated on the public UCI heart-disease dataset comprising 1050 patients and 14 attributes. By gathering a set of directly obtainable features from the heart-disease dataset, we considered this feature vector to be input for a DCNN to discriminate whether an instance belongs to a healthy or cardiac disease class. To assess the performance of the proposed method, different performance metrics, namely, accuracy, precision, recall, and the F1 measure, were employed, and our model achieved validation accuracy of 91.7%. The experimental results indicate the effectiveness of the proposed approach in a real-world environment.

Circular RNAs as a promising biomarker for heart disease

Heart-related diseases account for a high proportion of deadly pathologies, so a large number of studies have been conducted to discover potential biomarkers for the early detection of heart disease. Circular RNAs (circRNAs) are known to be involved in various human diseases, and several circRNAs associated with heart disease have been reported recently. However, there is a lack of literature summarizing the potential of circRNAs as biomarkers of heart disease and suggesting future research prospects. In this review, the history of circRNA research and important studies on circRNAs are described. In addition, the biogenesis process of circRNAs and the applicability of circRNA as a biomarker of heart disease are introduced, and select studies that monitored circRNAs circulating in human blood as biomarkers of heart disease are summarized. The contents of this review may help the researchers interested in the detection and prevention of heart disease at an early stage.

An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease.

Healthcare occupies a central role in sustainable societies and has an undeniable impact on the well-being of individuals. However, over the years, various diseases have adversely affected the growth and sustainability of these societies. Among them, heart disease is escalating rapidly in both economically settled and undeveloped nations and leads to fatalities around the globe. To reduce the death ratio caused by this disease, there is a need for a framework to continuously monitor a patient's heart status, essentially doing early detection and prediction of heart disease. This paper proposes a scalable Machine Learning (ML) and Internet of Things-(IoT-) based three-layer architecture to store and process a large amount of clinical data continuously, which is needed for the early detection and monitoring of heart disease. Layer 1 of the proposed framework is used to collect data from IoT wearable/implanted smart sensor nodes, which includes various physiological measures that have significant impact on the deterioration of heart status. Layer 2 stores and processes the patient data on a local web server using various ML classification algorithms. Finally, Layer 3 is used to store the critical data of patients on the cloud. The doctor and other caregivers can access the patient health conditions via an android application, provide services to the patient, and inhibit him/her from further damage. Various performance evaluation measures such as accuracy, sensitivity, specificity, F1-measure, MCC-score, and ROC curve are used to check the efficiency of our proposed IoT-based heart disease prediction framework. It is anticipated that this system will assist the healthcare sector and the doctors in diagnosing heart patients in the initial phases.

A Scoping Review of Machine Learning Techniques and Their Utilisation in Predicting Heart Diseases

Heart diseases are diverse, common, and dangerous diseases that affect the heart's function. They appear as a result of genetic factors or unhealthy practices. Furthermore, they are the leading cause of mortalities in the world. Cardiovascular diseases seriously concern the health and activity of the heart by narrowing the arteries and reducing the amount of blood received by the heart, which leads to high blood pressure and high cholesterol. In addition, healthcare workers and physicians need intelligent technologies that help them analyze and predict based on patients’ data for early detection of heart diseases to find the appropriate treatment for them because these diseases appear on the patient without pain or noticeable symptoms, which leads to severe concerns such as heart failure and stroke and kidney failure. In this regard, the authors highlight an amount of literature considered the most practical in utilizing machine learning techniques in predicting heart disease. Twenty articles were chosen out of fifty articles gathered and summarised in a table form. The main goal is to make this article a reference that can be utilized in the future to assist healthcare workers in studying these techniques with ease and saving time and effort on them. This article has concluded that machine learning techniques have a significant and influential role in analyzing disease data, predicting heart disease, and assisting decision-making. In addition, these techniques can analyze data that reaches millions of cohorts.