Heart Disease Prediction Research Articles

Precision Healthcare: A Deep Dive into Machine Learning Algorithms and Feature Selection Strategies for Accurate Heart Disease Prediction

Precision Healthcare: A Deep Dive into Machine Learning Algorithms and Feature Selection Strategies for Accurate Heart Disease Prediction

Prediction of Heart Disease by using Machine Learning Algortihms

Prediction of Heart Disease by using Machine Learning Algortihms

Genetic Algorithm-Based Multitier Ensemble Classifier for Diagnosis of Heart Disease

Designing a hybrid or ensemble data mining system appropriate to the application is a research challenge. Heart disease is a life threatening disease that need to be recognized correctly in the starting stage before it becomes more complex. Using artificial intelligence techniques in a hybrid and ensemble architecture can support the prediction of heart disease more effectively based on the given sample cases. This paper proposes a classification system called genetic algorithm-based ensemble classification system (GA-ECS) for the identification of heart disease. As feature selection is the crucial step before applying the data mining techniques, the genetic algorithm is used in GA-ECS to identify the best features in a given dataset. The Cleveland heart disease dataset is used for testing GA-ECS. The performance of GA-ECS is compared with different machine learning classifiers for the prediction of heart disease. GA-ECS showed a promising outcome with an accuracy of 90% for the diagnosis of heart disease.

A Novel Ensemble Approach with HGBDTRF for Enhanced Detection and Prediction of Heart Disease

Heart disease is responsible for around one-third of all deaths that occur throughout the globe, as shown by the statistics. The employ of machine learning headed for anticipate cardiac illness have emerged as an important technique for both treating and preventing the ailment as more research is carried out in this area. A unique method that we are working on is called Hybrid Gradient Boosting Decision Tree with Random Forest (HGBDTRF), and it is being developed via the use of ensemble learning in the research paper that we are now working on. Machine learning will be able to make more accurate predictions about heart disease as a result of this. It has been shown by the actual findings that the HGBDTRF algorithm is capable of achieving a prediction accuracy of 95% in the Cleveland cardiac disease dataset, which has 1322 samples.

CARDIO PREDICT: HARNESSING MACHINE LEARNING FOR ADVANCED HEART DISEASE RISK ASSESSMENT

Heart disease prediction using machine learning algorithms has gained significant attention due to its potential to improve diagnosis and treatment. This study explores various machine learning techniques and an algorithm applied to heart disease prediction. We analyze the performance of popular algorithms such as logistic regression, decision trees, random forests, support vector machines, and artificial neural networks on heart disease datasets. Additionally, we investigate the impact of feature selection, data preprocessing techniques, and model evaluation metrics on the predictive performance. The results demonstrate the heart disease risk, providing valuable insights for medical practitioner and researchers in the field of Cardiovascular health. The datasets used comprises a collection of patient data, including age, gender, blood pressure, cholesterol levels, and other relevant medical indicators. Neural networks are trained and evaluated to assess their performance in predicting the presence investigates the impact feature selection hyper parameter tuning. The results obtained provide insights into the strengths and limitations of different machine learning approaches for heart disease prediction, offering valuable guidance for healthcare practitioners and researchers in the field. Heart disease is prevalent and life-threatening condition worldwide. We analyze the performance of these algorithms using relevant metrics such as accuracy, precision, recall, and Fr-score. Additionally, we investigate feature importance to understand the factors contributing most to heart disease prediction. Our findings demonstrate the potential of machine learning in assisting healthcare professionals in early detection and prevention of heart disease, ultimately improving penitent outcomes and quality of life.

Electrocardiogram-based heart disease prediction using hybrid deep feature engineering with sequential deep classifier

Electrocardiogram-based heart disease prediction using hybrid deep feature engineering with sequential deep classifier

Machine Learning and Big Data Analytics for Precision Cardiac RiskStratification and Heart Diseases

The data explosion hasushered in a new era where insights are mined from vast data pools known as big data. Strategies for harnessing this data have emerged as critical decision-making tools across fields, employing various data analysis methods. Data mining techniques play an essential role in extracting meaningful patterns and insights. Thispaper focuses on the intersection of data mining and healthcare, particularly the critical concern of heart disease prediction.It presents a novel system that estimates heart attack risk, combining data mining with machine learning. Employing classification, the system stratifies data intotwo classes: heart disease presence or absence. Two powerful algorithms,decision tree classification and Naïve Bayes classification, enhance accuracy in predicting heart disease risk, achieving up to 91% and 87% accuracy, respectively. This review paper comprehensively analyzes the system's architecture, methodologies, and outcomes in healthcare, emphasizing data mining and machine learning's potential in medicine. Subsequent sections delve into methodology, results, and implications,providing a holistic view of this innovativeapproach.

Early detection and prediction of Heart Disease using Wearable devices and Deep Learning algorithms

Early detection and prediction of Heart Disease using Wearable devices and Deep Learning algorithms

Predicting Heart Disease Algorithm Using DNN and MNN in Deep Learning

Predicting Heart Disease Algorithm Using DNN and MNN in Deep Learning

Optimization heart disease prediction using independent component analysis and support vector machine

Prediction models play a crucial role in early detection and intervention for cardiac diseases. However, their effectiveness is often hindered by limitations inherent in current methodologies. This paper proposes a novel approach to address these challenges by integrating Independent Component Analysis (ICA) with the Support Vector Machine (SVM) technique. Utilizing a comprehensive Cleveland dataset, our model achieves notable performance metrics, including an accuracy of 90.16%, an Area Under the Curve (AUC) of 96.66%, precision of 90.02%, recall of 90.00%, F1-score of 90.00%, and a minimal log loss of 3.54. Our methodology not only surpasses previous methodologies through extensive comparative analysis but also addresses common constraints identified in existing literature. These limitations encompass insufficient feature representation, overfitting, and a lack of proactive intervention strategies. By amalgamating ICA with SVM, our model enhances feature extraction, mitigates overfitting, and facilitates proactive diagnosis and intervention in individuals suspected of having heart disease. This study underscores the importance of mitigating current literature limitations and underscores the potential of integrating contemporary machine-learning techniques to advance prediction models for heart disease.

Predicting Heart Diseases in IoT-Based Electronic Health Records: A Federated Learning Approach

Predicting heart diseases is important for finding them early and treating them effectively. We present a shared learning method for predicting heart diseases using IoT-based electronic health records (EHRs) in this work. Federated learning lets many autonomous IoT devices work together to train a model, while protecting the safety and security of the data. Proposed method uses the fact that IoT devices are spread out to train a global model for predicting heart disease without putting private EHR data in one place. With the EHR data, each IoT device learns a model locally and only sends model changes to a central computer. The computer takes all of these changes and improves the world model. This model is then sent back to the IoT devices to be improved even more. This looping process makes sure that the world model keeps getting better while keeping data private. The proposed method tested by using a real-world collection of EHRs from IoT devices in trials. We looked at how well our shared learning method worked compared to more standard centralized learning methods. Our results show that the pooled learning method makes predictions that are as good as or better than the other methods while protecting data privacy. It also looked at how different IoT device properties, like the amount of data they send and receive and their processing power, affect the shared learning process. It is discovered that devices with more processing power and more data add more to the improvement of the global model. This shows how important it is to choose the right devices in shared learning systems. The paper study shows that pooled learning can be used to predict heart diseases in IoT-based EHRs and that it works well. Our method uses the ability of IoT devices to work together to make accurate predictions while protecting data privacy. This makes it suitable for use in real-life healthcare situations.

An Effective Heart Disease Detection and Classification using Machine Learning

About 20.5 million people die yearly due to cardiovascular disease (CVD). Early prediction of heart disease and accurate heart severity identification can save the induvial life with timely medications. In many cases, most deaths occur due to inaccurate diagnosis of the heart. In computer science, various existing researchers have dealt with the classification of heart disease on synthetic as well as real-time datasets. But still, those systems have a challenge such as low classification accuracy, high error rate and inaccurate heart severity detection. After identifying all these challenges, we proposed an effective heart disease detection and classification using hybrid machine learning techniques. In this article, we describe how various feature extraction and hybrid machine learning classifiers produce accurate severity of heart disease on real-time datasets. In the first phase, we collected a Cleveland dataset from Kaggle and then applied preprocessing and normalization for data balancing. Various feature extraction and selection methods, such as TF-IDF, Co-relation coefficient, N-Gram, and bi-Gram features, are used for practical module training. The different machine learning we used on normalized datasets for classification. The five supervised machine learning algorithms are used, such as Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and Hybrid Machine Learning (HML) etc. The HML achieves 99.30% accuracy on the Cleveland dataset using Weka 3.8 machine learning framework. As a result, the proposed system compares with various heart disease predictions using machine learning techniques.

Adapting transformer-based language models for heart disease detection and risk factors extraction

Efficiently treating cardiac patients before the onset of a heart attack relies on the precise prediction of heart disease. Identifying and detecting the risk factors for heart disease such as diabetes mellitus, Coronary Artery Disease (CAD), hyperlipidemia, hypertension, smoking, familial CAD history, obesity, and medications is critical for developing effective preventative and management measures. Although Electronic Health Records (EHRs) have emerged as valuable resources for identifying these risk factors, their unstructured format poses challenges for cardiologists in retrieving relevant information. This research proposed employing transfer learning techniques to automatically extract heart disease risk factors from EHRs. Leveraging transfer learning, a deep learning technique has demonstrated a significant performance in various clinical natural language processing (NLP) applications, particularly in heart disease risk prediction. This study explored the application of transformer-based language models, specifically utilizing pre-trained architectures like BERT (Bidirectional Encoder Representations from Transformers), RoBERTa, BioClinicalBERT, XLNet, and BioBERT for heart disease detection and extraction of related risk factors from clinical notes, using the i2b2 dataset. These transformer models are pre-trained on an extensive corpus of medical literature and clinical records to gain a deep understanding of contextualized language representations. Adapted models are then fine-tuned using annotated datasets specific to heart disease, such as the i2b2 dataset, enabling them to learn patterns and relationships within the domain. These models have demonstrated superior performance in extracting semantic information from EHRs, automating high-performance heart disease risk factor identification, and performing downstream NLP tasks within the clinical domain. This study proposed fine-tuned five widely used transformer-based models, namely BERT, RoBERTa, BioClinicalBERT, XLNet, and BioBERT, using the 2014 i2b2 clinical NLP challenge dataset. The fine-tuned models surpass conventional approaches in predicting the presence of heart disease risk factors with impressive accuracy. The RoBERTa model has achieved the highest performance, with micro F1-scores of 94.27%, while the BERT, BioClinicalBERT, XLNet, and BioBERT models have provided competitive performances with micro F1-scores of 93.73%, 94.03%, 93.97%, and 93.99%, respectively. Finally, a simple ensemble of the five transformer-based models has been proposed, which outperformed the most existing methods in heart disease risk fan, achieving a micro F1-Score of 94.26%. This study demonstrated the efficacy of transfer learning using transformer-based models in enhancing risk prediction and facilitating early intervention for heart disease prevention.

A novel data Analysis architectural framework for data reduction on edges for Smart City Healthcare

The most difficult task is managing and digesting the massive amount of heterogeneous data created by IoT. It questioned the efficiency and computational storage capacity of today's infrastructure resources. Furthermore, because there is so much data, it becomes difficult to give the user enough information to make a decision. A technology known as Complex Event Processing (CEP) has been created to glean important insights from massive real-time data streams. The goal of this research is to create a system that can extract complicated events from large amounts of sensor data and only transmit those events to a cloud server. Global healthcare services have been expanding at an exponential rate because of the vast amount of data that clinical and medical organizations generate daily. Healthcare systems can use e-health services to meet the medical and assistive requirements of people. Edge technology is proven in this direction. Mixing the old classic concepts with the innovative we have proposed the Novel data analysis architecture for smart heart disease prediction. We have created the fuzzy-based inference system at the edges for the data analysis and used the resulting Complex Events at the end predictions.

Optimized Approach for Heart Disease Prediction by using Enhancing Diagnostic Method through Meta-Features

In today's world, pollution is worsening, and bad habits like not eating regularly, eating lots of junk food, and not exercising enough are becoming more common. This can cause health problems, and the global need for the detection of heart diseases is increasing. The American Heart Association, in 2023, has given an update that heart disease is the leading cause of death. Implementing machine learning models has given significant results, but due to the limitations of requirement balanced data, repeat training model complexity has led to unreliable results in some cases. Hence, the proposed model overcomes the limitations by comparing machine learning models for cardiovascular diseases. Performance of proposed work is evaluated with the base models KNN and DT with and without smooth. Thus, the comparison included increased accuracy in DT, but the proposed model GBM as a meta learner has led the performance metric with 92% accuracy, with recall of 0.89% and F1 score of 0.86%. Thus, the proposed approach has achieved the highest accuracy in cardiovascular diseases using meta-subjects. Future developments for the research will focus on applying the model to a larger dataset and analysing cases according to complex machine learning models.

Comprehensive evaluation and performance analysis of machine learning in heart disease prediction

Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the model’s robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the model’s predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The system’s sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes.

A Comparative Analysis using various algorithm Approaches to Enhance Heart Disease Prognosis

INTRODUCTION: Modern advancements in technology and data science have propelled the healthcare industry towards developing more accurate disease prognostic prediction models. Heart disease, being a leading cause of mortality globally, is a critical area of focus. This study delves into enhancing heart disease prognosis through a comprehensive exploration of various algorithmic approaches.
 OBJECTIVES: The objective of this paper is to compare and analyze different algorithmic techniques to improve heart disease prognosis using a dataset comprising data from over thirty thousand individuals obtained through Kaggle.
 METHODS: Techniques derived from social network analysis are employed to conduct this research. Data preprocessing, feature engineering, algorithm selection (including Stochastic Gradient Descent, AdaBoosting, Support Vector Machine, and Naive Bayes), hyperparameter tuning, model evaluation, and visualization are part of the systematic research process.
 RESULTS: The main results obtained in this paper include the identification of Naive Bayes as the most effective model for heart disease prognosis, followed by AdaBoosting, SVM, and Stochastic Gradient Descent. Performance evaluation metrics such as AUC, CA, F1, Precision, and Recall demonstrate the efficacy of these models.
 CONCLUSION: This research contributes to improving heart disease prognosis by leveraging algorithmic techniques and thorough analysis. The study envisions integrating the developed model into healthcare systems for widespread access to accurate heart disease prediction, with future plans to enhance data collection and model improvement for better outcomes.

Heart Disease Prediction: A Machine Learning Approach with Higher Accuracy

Heart disease, a prevalent cardiovascular condition, poses significant health risks and affects millions worldwide. The alarming rise in heart disease cases in recent years demands proactive measures, making early prediction of these conditions crucial and concerning. By employing machine learning techniques, this study aims to identify patients who are more susceptible to heart disease based on diverse medical attributes. The Heart Disease Dataset from Kaggle, consisting of 1025 samples and 14 features, was incorporated into this investigation. And after preprocessing the dataset by removing duplicate and null values and implementing statistical imputation and several data graphs, like a scatter plot, box plot, histogram, etc., we split it into training and testing datasets and apply SMOTE technique on the training one. Various machinelearning approaches were used in this study, out of which the optimized decision tree gave the best accuracy of 98.96%.

Development and Performance Evaluation of a Heart Disease Prediction Model Using Convolutional Neural Network

Heart disease is a leading cause of mortality globally and its prevalence is increasing year after year. Recent statistics from the World Health Organization show that about 17.9 million individuals are embattled with heart diseases annually and people under the age of 70 account for one-third of these deaths. Hence, there is need to intensify research on early heart disease prediction and artificial intelligence-based heart disease prediction systems. Previous heart disease prediction systems using machine learning techniques are unable to manage large amount of data, resulting in poor prediction accuracy. Hence, this research employs Convolutional Neural Networks, a deep learning approach for prediction of heart diseases. The dataset for training and testing the model was obtained from a government owned hospital in Nigeria and Kaggle. The resulting system was evaluated using precision, recall, f1-score and accuracy metrics. The results obtained are: 0.94, 0.95, 0.95 and 0.95 for precision, recall, f1-score and accuracy respectively. This show that the CNN-based model responded very well to the prediction of heart diseases for both negative and positive classes. The results obtained were also compared to some selected machine-learning models like Random Forest, Naïve Bayes, KNN and Logistic Regression and results show that the developed model achieved a significant improvement over the methods considered. Therefore, convolutional neural network is more suitable for heart disease prediction than some state-of-the-art machine-learning models. The contribution to knowledge of this research is the use of Afrocentric dataset for heart disease prediction. Future research should consider increasing the data size for model training to achieve improved accuracy.

Predicting Heart Diseases in IoT-Based Electronic Health Records: A Federated Learning Approach

Predicting heart diseases is important for finding them early and treating them effectively. We present a shared learning method for predicting heart diseases using IoT-based electronic health records (EHRs) in this work. Federated learning lets many autonomous IoT devices work together to train a model, while protecting the safety and security of the data. Proposed method uses the fact that IoT devices are spread out to train a global model for predicting heart disease without putting private EHR data in one place. With the EHR data, each IoT device learns a model locally and only sends model changes to a central computer. The computer takes all of these changes and improves the world model. This model is then sent back to the IoT devices to be improved even more. This looping process makes sure that the world model keeps getting better while keeping data private. The proposed method tested by using a real-world collection of EHRs from IoT devices in trials. We looked at how well our shared learning method worked compared to more standard centralized learning methods. Our results show that the pooled learning method makes predictions that are as good as or better than the other methods while protecting data privacy. It also looked at how different IoT device properties, like the amount of data they send and receive and their processing power, affect the shared learning process. It is discovered that devices with more processing power and more data add more to the improvement of the global model. This shows how important it is to choose the right devices in shared learning systems. The paper study shows that pooled learning can be used to predict heart diseases in IoT-based EHRs and that it works well. Our method uses the ability of IoT devices to work together to make accurate predictions while protecting data privacy. This makes it suitable for use in real-life healthcare situations.