Prediction of Cardiovascular Disease on Self-Augmented Datasets of Heart Patients Using Multiple Machine Learning Models (original) (raw)
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F1000Research, 2022
Background; Heart attack prediction is one of the serious causes of morbidity in the world's population. The clinical data analysis includes a very crucial disease i.e., cardiovascular disease as one of the most important sections for the prediction. Data Science and machine learning (ML) can be very helpful in the prediction of heart attacks in which different risk factors like high blood pressure, high cholesterol, abnormal pulse rate, diabetes, etc... can be considered. The objective of this study is to optimize the prediction of heart disease using ML. Methods: In this paper, we are presenting a machine learning-based heart attack prediction (ML-HAP) method in which the analysis of different risk factors and prediction for heart attacks is done using ML approaches of Support Vector Machines, Logistic Regression, Naïve Bayes and XGBoost. The data of heart disease symptoms has been collected from the UCI ML Repository and analysis has been performed on the data using ML methods. The focus has been on optimizing the prediction on the basis of different parameters. Results: XGBoost provided the best prediction among the four. The Area under the curve achieved with XGBoost is .94 and Logistic Regression is .92. The prediction with ML models in identifying heart attack symptoms is highly efficient, especially with boosting algorithms. The prediction was done to evaluate accuracy, precision, recall, and area under the curve. ML models are being trained to perform optimized predictions. Conclusions: This prediction can help clinically in analyzing the risk factors of the disease and interpretation of the patient scenario. Boosting the algorithm provided promising results to predict symptoms of heart disease. It can further be optimized by working further on risk factors associated with this condition. Open Peer Review Approval Status AWAITING PEER REVIEW Any reports and responses or comments on the article can be found at the end of the article.
HEART DISEASE PREDICTION USING ENSEMBLE APPROACH
Journal ijetrm, 2024
A heart attack, also known as a myocardial infarction, occurs when the flow of blood to a part of the heart muscle is suddenly blocked. This blockage prevents the heart from receiving enough oxygen. If blood flow is not promptly restored, the heart muscle begins to die. Machine learning algorithms are a powerful tool for predicting heart disease, allowing for more accurate results than traditional methods. Different models are compared and evaluated to identify the most accurate algorithm. Early detection is key to managing heart disease, and machine learning can help to identify at-risk individuals. Prevention of heart disease is invaluable for saving lives and reducing medical costs. Machine learning algorithms can provide the most accurate predictions possible, offering the best chance of preventing serious health issue
International Journal of Advanced Computer Science and Applications
Cardiovascular diseases (CVDs) remain a significant global health concern, demanding precise and early prediction methods for effective intervention. In this comprehensive study, various machine learning algorithms were rigorously evaluated to identify the most accurate approach for forecasting heart disease. Through meticulous analysis, it was established that precision, recall, and the F1-score are critical metrics, overshadowing the mere accuracy of predictions. Among the classifiers explored, the Decision Tree (DT) and Random Forest (RF) algorithms emerged as the most proficient, boasting remarkable accuracy rates of 96.75%. The DT Classifier exhibited a precision rate of 97.81% and a recall rate of 95.73%, resulting in an exceptional F1-score of 96.76%. Similarly, the RF Classifier achieved an outstanding precision rate of 95.85% and a recall rate of 97.88%, yielding an exemplary F1-score of 96.85%. In stark contrast, other methods, including Logistic Regression, Support Vector Machine, and K-Nearest Neighbor, demonstrated inferior predictive capabilities. This study conclusively establishes the combination of Decision Tree and Random Forest algorithms as the most potent and dependable approach for predicting cardiac illnesses, providing a groundbreaking avenue for early intervention and personalized patient care. These findings signify a significant advancement in the field of predictive healthcare analytics, offering a robust framework for enhancing healthcare strategies related to cardiovascular diseases.
Optimizing heart disease prediction through ensemble and hybrid machine learning techniques
International Journal of Electrical and Computer Engineering (IJECE), 2024
In this modern era, heart diseases have surfaced as the leading factor of fatalities, accounting for around 17.9 million lives annually. Global deaths from heart diseases have surged by 60% over the last 30 years, primarily because of limited human and logistical resources. Early detection is crucial for effective management through counseling and medication. Earlier studies have identified key elements for heart disease diagnosis, including genetic predispositions and lifestyle factors such as age, gender, smoking habits, stress, diastolic blood pressure, troponin levels, and electrocardiogram (ECG). This project aims to develop a model that can identify the best machine learning (ML) algorithm for predicting heart diseases with high accuracy, precision, and the least misclassification. Various ML techniques were evaluated using selected features from the heart disease dataset. Among these techniques, a combination of random forest (RF), multi-layer perceptron (MLP), XGBoost, and LightGBM employing an ensemble method with a stacking classifier, along with logistic regression (LR) as a metamodel, achieved the highest accuracy rate of 95.8%. This surpasses the efficiency of other techniques. The suggested method provides an encouraging framework for early prediction, with the overarching goal of reducing global mortality rates associated with these conditions.
Journal of Computer and Communications, 2022
Nowadays, machine learning is growing fast to be more popular in the world, especially in the healthcare field. Heart diseases are one of the most fatal diseases, and an early prediction of such disease is a vital task for many medical professionals to save their patient's life. The main contribution of this research is to provide a comparative analysis of different machine learning models to reach the most supporting decision for diagnosing heart disease with better accuracy as compared to existing models. Five models namely, K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boost (XGB), have been introduced for this purpose. Their performance has been tested and compared considering different metrics for precise evaluation. The comparative study has proven that the XGB is the most suitable model due to its superior prediction capability to other models with an accuracy of 91.6% and 100% on two different heart ailments datasets, respectively. Both datasets were acquired from the heart diseases repositories where dataset_1 was taken from the University of California, Irvine (UCI) and dataset_2 was from Kaggle.
Comparison Between Multiple Algorithms of Machine Learning to Predict Heart Attack
Comparison Between Multiple Algorithms of Machine Learning to Predict Heart Attack, 2023
Predicting and diagnosing cardiac disease has always been difficult and time-consuming for doctors. Medical facilities of all kinds are used to treat cardiac conditions. to avoid the high expensive of medical treatments and the most important is not to have heart attack. Predicting the onset of cardiac disease early on allows patients to receive treatment before the disease worsens, which is in everyone's best interest. In recent decades, heart disease has become epidemic, with excessive alcohol consumption, tobacco use, and inactivity as its primary causes. In this article, we use machine learning techniques to foresee cardiac illnesses. Various human healthrelated metrics are used to train and evaluate models. Artificial intelligence and ML are commonly used to foresee cardiac disorders. Various ML algorithms, including logistic regression, KNN, SVM, random forest, and decision tree, will be utilized, to comparison between these algorithms and choose the better one for predict heart attack. After the machine learning algorithm has been implemented, its effectiveness is analyzed and compared between multiple algorithms of machine learning.
IRJET, 2023
Heart disease is a significant health concern, warranting accurate prediction models for timely intervention. This research paper presents a comparative analysis of three popular machine learning algorithms, namely Logistic Regression, Support Vector Machines (SVM), and Random Forest, for heart disease prediction. Utilizing a comprehensive dataset encompassing clinical and lifestyle features, each model was developed and evaluated using standard metrics. The study unveils the most accurate and reliable algorithm for heart disease prediction, offering valuable insights into model performance. Furthermore, feature importance analysis sheds light on critical factors influencing accurate predictions. The results aid healthcare professionals in selecting the most appropriate model for efficient heart disease prediction, contributing to improved patient care and clinical decision-making. Random Forest achieved 88% accuracy, outperforming Logistic Regression and SVM for heart disease prediction.
Estimation of Prediction for Heart Failure Chances Using Various Machine Learning Algorithms
IRJET, 2023
The human heart is certainly the most important organ in our body. Our body cannot function normally if our heart cannot circulate enough blood to all our internal organs. Abnormalities in pumping blood causes heart failure. Though the term was coined in the 17th century it remains a global pandemic affecting over 26 million people worldwide. Hence predicting this deadly disease beforehand can do wonders for an individual as they can look after their health and fitness. Medical professionals find it difficult to come up with a scalable solution to predict the chances of heart failure. This is where advanced technologies like Machine learning can be used. With the help of Machine learning models, we can estimate if a person has chances of heart failure in the coming 10 years. In this study, we use a variety of machine learning methods to accurately predict heart failure. Here, we examined a dataset on heart failure that included significant pertinent medical data on 4238 patients. We have included the most crucial factors which play an important role in predicting if a person has chance of suffering from heart failure. We have implemented prediction models using various machine learning classification Algorithms. According to the findings of our study, in contrast with different machine learning algorithms, Random Forest had the greatest Accuracy = 96% as well as AUC = 99% when estimating the likelihood that patients would experience heart failure
Ensemble Based Heart Disease Prediction Using Machine Learning
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023
Clinical science has garnered significant attention from researchers due to their efforts in identifying early human mortality causes. The literature has confirmed that diseases can be caused by various factors, including heart-based disorders. To save human lives and assist healthcare professionals in recognizing, preventing, and managing heart disease, numerous researchers have proposed specific techniques.These techniques include the use of decision trees, random forests, XGBoost, and crossover models. The proposed approach dynamically analyzes the performance of each method, starting with the selection of the most appropriate strategy. The analysis involves implementing these approaches with different features to examine the statistics comprehensively. However, it is important to note that each successful plan has its own limitations. The goal is to build an intelligent and effective method through careful examination and refinement.
Ensemble technique to predict heart disease using machine learning classifiers
Network Biology, 2023
The exact forecast of heart disease is necessary to proficiently treat cardiovascular patients before a heart failure happens. Assuming we talk about AI techniques can be accomplished utilizing an ideal AI model with rich medical services information on heart diseases. To begin with, the feature extraction technique, gradient boosting-based sequential feature selection (GBSFS) is applied to separate the significant number of features (5, 7, 9, and 11) from coronary illness dataset to create important medical services information. The stacking model is prepared for coronary illness forecast. A comparison model is made between datasets with prominent features (5, 7, 9, and 11) as well as all features. The proposed framework is assessed with coronary illness information and contrasted and customary classifiers in view of feature elimination include determination strategies. The proposed framework acquires test accuracy of 98.78%, which is most noteworthy in marking model with 11-featuers and higher than existing frameworks. This outcome shows that our framework is more powerful for the expectation of coronary illness, in contrast with other cutting edge strategies.