A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques

Data mining techniques have been widely used in clinical decision support systems for prediction and diagnosis of various diseases with good accuracy. These techniques have been very effective in designing clinical support systems because of their ability to discover hidden patterns and relationships in medical data. One of the most important applications of such systems is in diagnosis of heart diseases because it is one of the leading causes of deaths all over the world. Almost all systems that predict heart diseases use clinical dataset having parameters and inputs from complex tests conducted in labs. None of the system predicts heart diseases based on risk factors such as age, blood pressure, fasting blood sugar, chest pain etc. Heart disease patients have lot of these visible risk factors in common which can be used very effectively for diagnosis. System based on such risk factors would not only help medical professionals but it would give patients a warning about the probable presence of heart disease even before he visits a hospital or goes for costly medical check-ups. Hence this paper presents a technique for prediction of heart disease using major risk factors. This technique involves two most successful data mining tools, Support vector machine and Principal component analysis. The hybrid system implemented uses the global optimization advantage PCA for initialization of neural network weights. The learning is fast, more stable and accurate as compared to back propagation.

Heart disease has long been a major threat to human health, causing about one-third of all deaths each year. Therefore, there is a need for accurate and effective prediction of cardiac disease using machine learning techniques. This paper uses data from 1,319 patients with heart disease and applies several machine-learning methods to study the relationship between a total of eight factors. The algorithms used in this paper include neural networks, decision trees, random forests, and so on. The best model was established by the random forest method, with an accuracy of 96.97%, precision of 97.03%, recall of 96.97%, and f1-Score of 0.9. It was found that troponin and CK-MB indicators had the highest influence weights, and the sum of the weights of each model accounted for more than 75% of the total weight, which demonstrated their significance in the prediction of heart disease, and the results could be utilized for the future prediction of heart disease. In addition, it also plays an essential role in the prediction of heart disease. To sum up, this is really important for the prediction of future heart disease.

In modern society, mortality and morbidity are caused majorly by heart disease (HD), and in world, deaths are mainly caused by heart disease (HD). The detection of HD and prevention against death is a challenging task. Medical diagnosis is highly complicated and it is very important. It must be performed efficiently with high accuracy. The professionals in healthcare in heart disease diagnosis are assisted by using various techniques in data mining. In this work, heart disease prediction method with following steps is introduced. The steps are preprocessing technique, feature selection, and learning algorithm. Before that important features are selected via the use of the updated frequency-based bat algorithm (UFBBA). In the UFBBA algorithm, the frequency values are computed via the use of the features. If the features are most important, then the frequency is higher else the frequency is lower. A selected feature from the UFBBA is used for better accuracy results than the other classifiers. A feature selected from the algorithm is applied for classification (Vote). Experimentation dataset of the proposed system is collected from Irvine (UCI) Cleveland dataset, University of California dataset. The results are measured with respect to accuracy, f-measure, precision, and recall.

Cardiovascular sickness is the largest cause of death in developing countries. The study of this coronary heart disease prediction model using a data mining technique and decision tree algorithm are applied in medical research, especially in heart disease prediction. The Coronary Heart Disease is also known as Coronary Artery Disease (CAD). Hence the decision system is analyses the heart disease for the patient. In this paper coronary illness studied a more number of input attributes and database records based on the patient's clinical data. This paper focuses on the around the prediction of heart disease accuracy value using the decision tree algorithm.

Cardiovascular diseases remain one of the leading causes of mortality worldwide, particularly in low- and middle-income countries. Early and accurate prediction of heart disease is essential for timely intervention and improved patient outcomes. Machine learning techniques offer promising solutions; however, challenges such as class imbalance, lack of interpretability, and limited real-world validation persist. In this study, a machine learning–based heart disease prediction framework was developed using a real-world clinical dataset comprising 5000 patient records collected from healthcare facilities in Bangladesh. Data preprocessing included cleaning, feature encoding, train–test splitting, and class imbalance handling using the Synthetic Minority Oversampling Technique (SMOTE). Multiple machine learning models—Logistic Regression, Decision Tree, Support Vector Machine, and Random Forest—were evaluated using 10-fold stratified cross-validation. Model performance was assessed using accuracy, precision, recall, and F1-score. SHAP (SHapley Additive exPlanations) was employed to enhance model interpretability. The best-performing model was deployed as a web-based decision support system. Among the evaluated models, the Random Forest classifier achieved the best performance, with an accuracy of 98%, recall of 96%, and F1-score of 96%. Ablation studies demonstrated the effectiveness of SMOTE, feature integration, and ensemble modeling. SHAP analysis identified clinically relevant features contributing to heart disease prediction, enhancing transparency and trust in model decisions. The proposed framework provides an accurate, interpretable, and practical solution for heart disease prediction using real-world clinical data. The integration of explainable machine learning and web-based deployment highlights its potential for clinical decision support. Future work will focus on multi-center prospective validation and adaptive model updating to further improve generalizability and real-world applicability. • A large real-world clinical dataset of 5000 patients was used for robust heart disease prediction. • A Random Forest model with SMOTE achieved high performance (98% accuracy, 96% recall). • Comprehensive ablation and model comparison analyses validate methodological robustness. • SHAP-based explainability provides transparent and clinically interpretable predictions. • The proposed system is deployed as a web-based clinical decision support tool.

Data mining is a way of searching for information from large amounts of data for the purposes of various applications. Several techniques in data mining can be used for association, classification, clustering, prediction, and sequential modeling. Machine learning is used in medical science to help medical teams find out the condition of patients with heart disease. A lot of machine learning still has limited predictive capabilities, and is incompatible. This study uses different machine learning techniques, namely PSO-based SVM, Neural Network, Decision Tree, Naïve Bayes and SVM to assist in building, understanding and interpreting different models of heart disease diagnosis. The use of the pso-based svm algorithm in the prediction of heart disease shows a 100% greatest accuracy than the Decision Tree, only 88.68% and Naïve Bayes of 82.15%, Neural Network with an accuracy of. 95.71%, SVM with an accuracy of 99.71%. The results of this study are expected to be beneficial for the world of health and for researchers who use machine learning techniques.

Heart disease is one of the leading cause deaths of worldwide. Prediction of cardiovascular infection is a critical challenge in the area of clinical data analysis. Data mining techniques are providing an effective decision and significant results on data that are used widely for predicting. The purpose of this paper is to propose a novel approach with aims to find a noteworthy method to diagnose heart disease prediction. In this research, a unique dataset was created by combining the Cleveland dataset and Stalog heart disease datasets collected from the UCI ML repository. The new dataset contains 14 medical parameters such as age, sex, blood pressure, and 568 instances for training and prediction heart disease. This paper offers a novel methodology of NNDT (Neural Network and Decision Tree) that uses Neural Network for training model and Decision Tree to test classification for better heart disease prediction. The performance of the proposed approach have been compared with Naive Bayes, Support Vector Machine, Neural Network, Voted Perceptron, and Decision Tree algorithms. The results showed that the accuracy and performance improved as compared to other techniques and methods. This study enables the researchers to analyze the heart disease data with a new approach to predict heart diseases to maintain human health.

<p>For the past two decades, heart disease has been classified as one of the main causes of mortality globally. Fortunately, most researchers focused on data mining techniques, which play an important role in accurately predicting heart disease to develop their models. In this paper, by combining particle swarm optimization (PSO) and modified random forest (MRF), a new approach (PSO-MRF) is proposed to predict heart disease. The main purpose is to select the important features after the bootstrap method for each decision tree in the random forest, and then optimize the MRF by the PSO algorithm. The experiments are carried out using the publicly accessible UCI heart disease datasets. Thorough experimental analysis demonstrates that our approach has outperformed the random forest algorithm as well as many other classifiers. This model helps doctors and researchers improve the diagnosis and treatment of heart disease, resulting in more prompt, accurate patient care.</p>

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.

Blood circulation in the body is very crucial for life. So, one of the most important parts of our body is the heart since it circulates blood to every part of our body. Any damage to the heart can be life-threatening so, we need a dependable and precise error-free system for the proper treatment using machine learning techniques and big data. There are several machine learning techniques that can be used to predict heart-related disease at an early stage to stop it from becoming fatal. Some of the machine learning techniques with higher accuracy are SVM (support vector machine), naïve Bayes, decision tree, KNN (K-nearest neighbor). Big data technologies are extremely important for heart disease prediction. The high cost, complex study of modern medicines, and limited data available of a small population of patients create problems in improving quality care and research. The big data approach gathers this kind of large number of health records and data of wide variety, high volume, and utmost acquisition speed. By combining these large datasets researchers aim to build algorithms for better accuracy and prediction of heart-related disease. So in this research, a comparative study is performed for heart diseases using large dataset using various machine learning techniques like SVM, artificial neural network, naïve Bayes, and KNN to find the best suitable technique for early heart disease prediction.

The aim of the study is to predict heart disease by using naive bayes technique and to increase the accuracy in prediction using machine learning classifiers by comparing their performance. Two groups such as Naive Bayes and K-Nearest Neighbour (KNN) are analysed in this research. The algorithms have been implemented and tested over a dataset which consists of 1700 records. Sample size is found to be 540 from clincalc.com with a pretest power of 80%. 20 samples are analysed for statistical analysis. After performing the experiment the mean accuracy is 82.47% by using Naive Bayes algorithm and 79.64% by using k-nearest neighbour algorithm for prediction of heart disease. There is a statistical significant difference in accuracy for two algorithms the p¡O.05 by performing independent samples t-tests. This research is to improve the prediction of heart disease by using machine learning algorithms. Performance and accuracy in prediction of heart disease is carried out for the two algorithms. The comparison results show that Naive Bayes have better performance compared to KNN.

Heart disease is among the conditions that people suffer from most frequently. Millions of people worldwide pass away each year as a result of it, making it one of the main causes of mortality. Heart disease can be characterised by issues with the heart valves, heart failure, arrhythmias, and coronary artery disease. Heart disease comes in more than 30 distinct forms. By allowing for prompt intervention and the right kind of care, early and precise cardiac disease prediction can greatly improve patient outcomes. In this model, we investigate the application of machine learning techniques for anticipating cardiac disease. We investigate a large dataset made up of patient details, such as demographics, medical histories, and clinical measures. It is absolutely mind-blowing to think that machine learning algorithms could one day properly forecast when cardiac disease will start and how to diagnose it. Machine learning techniques including logistic regression, decision trees, XGBoost, gradient boosting, random forests, support vector machines (SVMs), and artificial neural networks (ANNs) are utilised to construct predictive models. A hybrid model including ANN, gradient boost, Decision Tree, SVM, random forests, & Logistic Regression makes up the forecasting model. To increase the model's accuracy. To manage missing values, normalise features and solve class imbalance. The dataset has been pre-processed. The best accurate heart disease predictions are found using feature selection approaches. Area under the receiver's operating characteristic curve, recall, accuracy, and precision are some of the performance measures used for training and evaluating the models. The major goal of this model is to put out a novel strategy for creating a model that successfully solves practical issues

The IoT has applications in many areas such as manufacturing, healthcare, and agriculture, to name a few. Recently, wearable devices have become popular with wide applications in the health monitoring system which has stimulated the growth of the Internet of Medical Things (IoMT). The IoMT has an important role to play in reducing the mortality rate by the early detection of disease. The prediction of heart disease is a key issue in the analysis of clinical dataset. The aim of the proposed investigation is to identify the key characteristics of heart disease prediction using machine learning techniques. Many studies have focused on heart disease diagnosis, but the accuracy of the findings is low. Therefore, to improve prediction accuracy, an IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed. The proposed MSSO-ANFIS improves the search capability using the Levy flight algorithm. The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima. The learning parameters are optimized utilizing MSSO to provide better results for ANFIS. The following information is taken from medical records to predict the risk of heart disease: blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, etc. The heart condition is identified by classifying the received sensor data using MSSO-ANFIS. A simulation and analysis is conducted to show that MSSA-ANFIS works well in relation to disease prediction. The results of the simulation demonstrate that the MSSO-ANFIS prediction model achieves better accuracy than the other approaches. The proposed MSSO-ANFIS prediction model obtains an accuracy of 99.45 with a precision of 96.54, which is higher than the other approaches.

The existing data for clinical diagnosis are often enlarged, but available tools are not efficient enough for decision making. Data mining techniques provide a user-oriented approach for clinical diagnosis and reduce risk factors. To improve clinical diagnosis, particularly for heart diseases, nine different data mining techniques have been applied for classification and clustering. We compare all these techniques for better prediction. Despite all recent research efforts, the literature lacks the application of multiple techniques on multiple data sets for heart disease prediction; which helps in decision making. In particular, this study is the augmentation of techniques for multiple data analysis by comparing four datasets with 14 attributes and a different number of instances. Another challenge is how to increase the accuracy of the decision-making process. Our research findings predict the better accuracy by using SMO and classification via regression for all data sets which shows the significant difference. Consequently, this research further helps to integrate the clinical decision support, thereby reducing medical errors, enhance patient safety, decrease unwanted practice variation, and improve patient recovery.

Heart disease prediction by using novel optimization algorithm: A supervised learning prospective