Heart Disease Prediction Model Research Articles

Optimization of multidimensional feature engineering and data partitioning strategies in heart disease prediction models

The relentless rise in heart disease incidence, a leading global cause of death, presents a significant public health challenge. Precise prediction of heart disease risk and early interventions are crucial. This study investigates the performance improvement of heart disease prediction models using machine learning and deep learning algorithms. Initially, we utilized the Heart Failure Prediction Dataset from Kaggle. After preprocessing to ensure data quality, three distinct feature engineering techniques were applied: PCA for dimensionality reduction, ET for feature selection, and Pearson's correlation coefficient for feature selection. We assessed their impact on model performance. The dataset was then partitioned into three different data split ratios—1:9, 2:8, and 3:7—to determine their specific effects on model performance. Twelve machine learning classifiers—LGBM, Adaboost, XGB, RF, DT, KNN, LR, GNB, ET, SVC, GB, and Bagging—were trained and evaluated based on five key metrics: accuracy, recall, precision, F1 score, and training time. The influence of different feature engineering methods and data partitioning ratios on model performance were systematically analyzed using paired-sample t-tests. Among the feature engineering methods compared, the Bagging classifier, when combined with feature selection via ET, exhibited superior performance. It achieved an accuracy of 97.48 % and an F1-Score of 97.48 % with a data split ratio of 1:9 between the test and training sets. With a 2:8 split, the accuracy was 94.96 % and the F1-Score was 94.95 %. For a 3:7 split, the accuracy was 94.12 % and the F1-Score was 94.11 %. Paired sample T-test results indicate that feature selection using Pearson correlation coefficient can shorten training duration, but this also leads to a decline in classifier performance. After applying PCA dimensionality reduction, compared with the control group, there was no significant difference in the training efficiency and efficacy of the classifier. However, feature selection through ET significantly reduced the training time for various classifiers while ensuring their performance.

Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction

This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques—Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection—play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts.

Heart disease prediction: Improved quantum convolutional neural network and enhanced features

Currently, heart disease is the leading cause of death in world. Since cardiac sickness requires knowledge and detailed information, it is challenging to anticipate. Healthcare systems have been using Internet of Things (IoT) technologies for gathering sensor data for diagnosing the heart disease and prognosis in recent years. Researchers have focused a lot of emphasis on diagnosing heart disease, although the outcomes are not always reliable. This article proposes the automated heart disease prediction model with three main stages, including preprocessing, feature extraction, and prediction. The input data undergoes an improved Z-score normalization as the preprocessing step. The appropriate features needed to train the prediction model are retrieved from the preprocessed data during feature extraction. The features extracted include improved entropy, statistical features, and information gain features. Depends on the features extracted, prediction is determined by the Improved Quantum CNN (IQCNN). The results of the IQCNN are compared to earlier systems for a various metrics. The proposed IQCNN model has achieved better accuracy of 0.91 at a 70% learning rate when evaluated over conventional methods like Bi-LSTM, CNN, QCNN, DNN, NN, RNN, MDCNN and E-KNN for better performance in the prediction of heart disease.

Hybrid optimized temporal convolutional networks with long short-term memory for heart disease prediction with deep features

A heart attack is intended as top prevalent among all ruinous ailments. Day by day, the number of affected people count is increasing globally. The medical field is struggling to detect heart disease in the initial step. Early prediction can help patients to save their life. Thus, this paper implements a novel heart disease prediction model with the help of a hybrid deep learning strategy. The developed framework consists of various steps like (i) Data collection, (ii) Deep feature extraction, and (iii) Disease prediction. Initially, the standard medical data from various patients are acquired from the clinical standard datasets. Here, a One-Dimensional Convolutional Neural Network (1DCNN) is utilized for extracting the deep features from the acquired medical data to minimize the number of redundant data from the gathered large-scale data. The acquired deep features are directly fed to the Hybrid Optimized Deep Classifier (HODC) with the integration of Temporal Convolutional Networks (TCN) with Long Short-Term Memory (LSTM), where the parameters in both classifiers are optimized using the newly suggested Enhanced Forensic-Based Investigation (EFBI) inspired meta-optimization algorithm. Throughout the result analysis, the accuracy and precision rate of the offered approach is 98.67% and 99.48%. The evaluation outcomes show that the recommended system outperforms the extant systems in terms of performance metrics examination.

Evaluation of machine learning techniques for heart disease prediction using multi-criteria decision making

Globally, heart disease is the primary cause of death. Early detection of this disease enables cardiologists to make more accurate judgments regarding the health of their patients. Due to machine learning’s ability to identify patterns in data, its use in the medical industry has increased. Many heart disease prediction models have been developed by various researchers utilizing machine learning techniques (MLTs). The performance of MLTs on heart disease prediction may vary for different accuracy measures. Thus, the choice of the appropriate machine-learning technique for heart disease prediction is a challenging task. This paper proposes a multi-criteria decision-making (MCDM)-based method to evaluate the MLTs for heart disease prediction considering various performance measures taken into account altogether. The proposed approach uses the concept of a combined compromise solution (CoCoSo)- an MCDM method. For validation of the proposed approach, an experimental study was conducted to evaluate the performance of fifteen machine learning techniques for predicting heart disease over three heart disease datasets considering six performance measures taken into account altogether. Results show that the logistic regression and support vector machine are recommended as the most suitable MLTs for heart disease prediction modeling with respect to six performance measures considered simultaneously.

Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2 T and adaptive boosted decision fusion with babysitting refinement.

Global Cardiovascular disease (CVD) is still one of the leading causes of death and requires the enhancement of diagnostic methods for the effective detection of early signs and prediction of the disease outcomes. The current diagnostic tools are cumbersome and imprecise especially with complex diseases, thus emphasizing the incorporation of new machine learning applications in differential diagnosis. This paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model. On the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease. These findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model's use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study.

Development of Prediction Model for Heart Disease by Combining Clustering and Classification Techniques

The concerning trends in deaths related to heart disease some measures need to be in place to ensure early treatment and diagnosis of the disease. Therefore, one of the way can be done is by leveraging the abundance of medical data available. Advancement in technology today has improved the availability and accessibility huge amounts of valuable data and it only makes sense for us to explore the opportunities that lie in the data that could possibly save lives and reduce costs. Thu, this study aims to do that with the help of classification and clustering data mining techniques to predict heart disease based on some key indicators of the disease. Studies show that applying classifiers on clustered data can improve the performance of algorithms. Hence, this method will be explored in this study using the Naïve Bayes, Decision Tree and Random Forest classifiers together with both K-Means Clustering and Density-Based Clustering on the data analysis using tool WEKA. The performance of the each model will be measured and compared against each other using accuracy, precision, recall, specificity, AUC and model build time. Thus, this paper will focused on development of prediction model for heart disease by combining clustering and classification techniques in detail.

HEART DISEASE PREDICTION USING MACHINE LEARNING

Heart disease is a major cause of death worldwide, making early diagnosis and prevention essential. Predictive models have gained significant attention in recent years, with several algorithms being employed to develop these models. However, there are challenges in implementing heart disease prediction models, including data quality, model accuracy, ethical concerns, and limited data. Therefore, this project aims to develop a heart disease prediction model and analyse different algorithms used in disease prediction. In order to increase the predictive accuracy of machine learning algorithms, this study compares six algorithms, including KNN (K-Nearest Neighbour), Decision Tree, Random Forest, Support Vector Machines, Logistic Regression, and Neural Network. 13 attributes, including age, sex, and cholesterol, are used, and ensemble methods like boosting and bagging are used. The accuracy, recall, f1 score, and precision of each algorithm are calculated to determine the most accurate model. Additionally, this study identifies the limitations of heart disease prediction models and their implications for patient diagnosis and treatment, by developing and analysing heart disease prediction models. In conclusion, while heart disease prediction models have the potential to be financially feasible and be useful in the future, their current limitations and challenges mean that they cannot be relied upon as the sole means of diagnosis or treatment decisions Key Words: Heart Diseases, Machine Learning Algorithms, Logistic Regression, Random Forest, Decision Tree.

Heart disease prediction (HDP) using Artificial Intelligence and IoMT for intelligent healthcare models

The present growth in Internet of Medical Things (IoMT) and Artificial Intelligence (AI) paved a way for advanced healthcare systems from conventional methods. The integration of AI and IoMT provides varied chances in medical domain. With that concern, the proposed model derives a novel model for Heart Disease Prediction (HDP), incorporates IoMT and AI. The proposed model comprises of different phases of functions, as, data collection, data preparation, feature optimization and selection, classification. IoMT devices include medical or wearable sensors are used for continuous collection of medical statistics while machine learning model process the data for disease prediction. Here, a new feature selection model called Enhanced Binary Particle Swarm Optimization (EBPSO) for reducing joint feature selection problems. With the extracted features, classification is performed with Cascaded Long Short Term Memory (CLSTM) model for attaining better accuracy of medical data classification. During evaluation, the proposed HDP model achieved the maximal accuracy in disease prediction. Hence, the model can be effectively used for diagnosing heart disease in Smart Healthcare Models.

Prediction Model for Diagnosing Heart Disease Using Classification Algorithm

Heart disease often causes death if not treated quickly and appropriately. Early diagnosis can prevent more serious complications and treat heart disease patients best. The existence of a disease prediction model can help health workers to diagnose diseases more quickly and accurately. The heart disease prediction model using a classification algorithm is a system built using machine learning techniques. The classification algorithm chosen is NN, Naive Bayes, Random Forest, and SVM because it is the best algorithm for predicting heart disease. This study makes a comparison of the four algorithms using a dataset of 918 instances with 11 features. The result is that the Random Forest algorithm produces the highest accuracy, with 86.8%, and has the best ability to distinguish classes based on the ROC curve.

Perbandingan Kinerja Akurasi Model Mesin Learning Untuk Prediksi Penyakit Jantung

This research aims to comprehensively analyze heart disease-related data through Exploratory Data Analysis (EDA), identification of correlations between numerical variables, and cluster analysis to uncover patterns in the data. Furthermore, using various machine learning algorithms, such as Logistic Regression, Support Vector Classifier, Decision Tree Classifier, Random Forest Classifier, K-Nearest Neighbors, and Gaussian Naive Bayes, a heart disease prediction model was built. The model evaluation shows that Naive Bayes has the highest test accuracy of 90%, followed by RandomForestClassifier and KNeighborsClassifier which have 85% test accuracy. These findings indicate a good ability to predict heart disease, but further analysis is needed to ensure good generalization to unseen data. This research makes an important contribution to the development of heart disease prediction models and can support early detection and appropriate intervention strategies.

Heart Disease Prediction using Hybrid Machine Learning

heart disease usually refers to conditions such as narrowed or blocked blood vessels leading to heart failure, pain due to decreased blood flow to the heart (angina), or stroke. Heart disease is one of the leading causes of mortality in the world today. It contributes to about 30% of all deaths worldwide. Predicting cardiovascular disease is a critical challenge in the field of clinical data analytics. Machine learning (ML) has been shown to be effective in making decisions and predictions from the large amount of data generated in the healthcare industry. Machine learning is being used in many fields around the world. The healthcare industry is not exempt. Machine learning can play a significant role in predicting musculoskeletal conditions, heart disease and more. Such information, when predicted in a timely manner, can provide physicians with important clues so they can tailor their diagnosis and treatment to each patient. We are working on predicting potential heart disease in humans using machine learning algorithms. In this work, we present a novel method for identifying important variables that improve the precision of “cardiovascular disease” prediction. Along with a number of feature combinations and well-known classification techniques, the "prediction" model is introduced. When preprocessing the data in an efficient way we can achieve better performance. Preprocessing means handling missing values, removing outliers, balancing the data, scaling the feature, and optimizing the hyperparameter for the tree, selecting the important feature using the feature score technique. Experiment conducted shows that preprocessing technique helps us to get better performances. The experiment result also shows that hybrid random forest with linear model and feature score (HRFLMFS) prediction model for heart disease gives us a better result.

Enhanced Recurrent Neural Network (RNN) For Heart Disease Risk Prediction Using Framingham Datasets

Heart disease is one of the leading causes of death globally, which takes 17.9 million lives each year. The existing heart disease prediction techniques have a gap that does not consider the smoking attributes from the heart disease data. So, the accuracy is based on the limited number of medical data and the deep learning model. The existing deep learning models which use the Recurrent Neural Network (RNN) for heart disease prediction consume more processing and analysing time, mainly due to the delay of data retrieval. This delay causes the prediction process to become slower and leads to a moderate prediction only. The backpropagation of the RNN with an update gate and internal memory to carry the updated data cause a minor data glitch that leads to lower accuracy. Therefore, an efficient heart disease prediction model is very crucial to provide early detection among patients. This research proposes a Heart Disease Risk Prediction Model (HDRPM) with an enhanced RNN to improve the prediction accuracy using Framingham heart disease datasets. The specificity and sensitivity are imposed to improve the quality of the predictions. Sensitivity measure is used for detecting patients with heart disease perfectly and specificity measure is used for detecting patients without the disease perfectly. Besides the accuracy and quality of the prediction problem, the imbalance of minority classes in the dataset occurred in most deep learning prediction fields. This research aims to improve the quality of imbalanced Framingham datasets using Synthetic Minority Over-sampling Technique (SMOTe), which will synthetic instances in a small class to be equalized. The existing RNN model faces vanishing gradients that impede the learning of long data sequences. These gradients that carry information in the RNN cells will become smaller gradually till it minimises the parameter updates and leads to poor learning. For this purpose, the presence of multiple Gated Recurrent Unit (GRU) is used to overcome the vanishing gradients and ensure the hidden layers. The neurons of RNN rapidly cater for the essential information during the training and validation phase of the HDRPM. The integration of multiple GRU with the RNN, operating on the Tensorflow as back-end and Keras as the core for the neural network library has increased the performance of the proposed model. The proposed model provides up to 98.78%, the highest accuracy achieved compared to related previous work, which is a quantum neural network model with 98.57. This HDRPM is expected to significantly contribute to early detection of heart disease patients.

An Analysis of Heart Disease Prediction using Machine Learning

Heart disease is a major cause of death worldwide, making early diagnosis and prevention essential. Predictive models have gained significant attention in recent years, with several algorithms being employed to develop these models. However, there are challenges in implementing heart disease prediction models, including data quality, model accuracy, ethical concerns, and limited data. Therefore, this project aims to develop a heart disease prediction model and analyze different algorithms used in disease prediction. In order to increase the predictive accuracy of machine learning algorithms, this study compares six algorithms, including KNN (K-Nearest Neighbor), Decision Tree, Random Forest, Support Vector Machines, Logistic Regression, and Neural Network. 13 attributes, including age, sex, and cholesterol, are used, and ensemble methods like boosting and bagging are used. The accuracy, recall, f1 score, and precision of each algorithm are calculated to determine the most accurate model. Additionally, this study identifies the limitations of heart disease prediction models and their implications for patient diagnosis and treatment, by developing and analyzing heart disease prediction models. In conclusion, while heart disease prediction models have the potential to be financially feasible and be useful in the future, their current limitations and challenges mean that they cannot be relied upon as the sole means of diagnosis or treatment decisions.

Grid Search based Optimum Feature Selection by Tuning hyperparameters for Heart Disease Diagnosis in Machine learning

Background: Heart disease prediction model helps physicians to identify patients who are at high risk of developing heart disease and target prevention strategies accordingly. These models use patient demographics, medical history, lifecycle factors, and clinical measurements to calculate the risk of heart disease within a certain time frame. In identifying important features of heart disease, a popular approach is using Machine learning (ML) models. ML models can analyse a large amount of data and find patterns that are difficult for humans to detect. Methods: In this proposed work, Random Forest classifier is used to identify the most important features that contribute to heart disease and increase the prediction accuracy of the model by tuning the hyperparameters using grid search approach. Results: The proposed system was evaluated and compared in terms of accuracy, error rate and recall with the traditional system. As the traditional system achieved accuracies between 81.97% and 90.16%., the proposed hyperparameter tuning model achieved accuracies in the range increased between 84.22% and 96.53%. Conclusion: These evaluations demonstrated that the proposed prediction approach is capable of achieving more accurate results compared with the traditional approach in predicting heart disease by finding optimum features.

Heart Disease Prediction Using Support Vector Machine and Artificial Neural Network

Heart-related illnesses, often known as cardiovascular diseases, have been the leading cause of mortality globally over the past several decades and are now recognized as the most major illness in both India and the rest of the globe. The severity out of the disease can be avoided with proper care at proper stage. This disease claims early and accurate prediction to avoid causalities. As proper medical support is not adequate, diseases are not being identified at the proper time and treatment cannot be started. Machine learning algorithms have shown promise in predicting heart disease risk based on patient data. In this study, a machine learning-based heart disease prediction model has been presented. The objective of the work is to build a machine learning-based model for early and adequate prediction of heart disease. The proposed model has utilized support vector machine and artificial intelligence with an accuracy of 81.6% and 86.6%, respectively. The findings show that the model predicts heart disease risk with excellent accuracy, sensitivity, and specificity, offering healthcare professionals a useful tool to pinpoint people who may be more at risk of developing heart disease.

Design of an efficient decision support system using evolutionary deep forward network model

With the vast advancements in the medical domain, earlier prediction of disease plays a substantial role in enhancing healthcare quality and making better decisions during tough times. This research concentrates on modelling and automated disease prediction model to offer an earlier prediction model for heart disease and the risk factors. This work considers a standard UCI machine learning-based benchmark dataset for model validation and extracts the risk factors related to the disease. The outliers and imbalanced datasets are pre-processed using data normalization to enhance the classification performance. Here, feature selection is performed using non-linear Particle Swarm Optimization (NL - PSO). Finally, classification is done with the Improved Deep Evolutionary model with Feed Forward Neural Networks (IDEBDFN). The algorithm’s learning nature is used to evaluate the nature of the hidden layers to produce the optimal results. The outcomes demonstrate that the anticipated model provides superior prediction accuracy. The simulation is carried out in a MATLAB environment, and metrics like accuracy, F-measure, precision, recall, and so on are evaluated. The accuracy (without features) of the evolutionary model in the UCI ML dataset is 97.65%, accuracy (with features) is 98.56%, specificity is 95%, specificity is 2% higher than both the datasets, F1-score is 40%, execution time (min) is 0.04 min, and the AUROC is 96.85% which is substantially higher than other datasets. The proposed model works efficiently compared to various prevailing standards and individual approaches.

Heart Disease Prediction Model based on Prophet

Heart disease is one of the major causes of death for people of all races, genders, and nationalities. In the United States, for instance, heart disease causes more than 600,000 deaths every year and is the largest leading cause of death in 2020. A reliable heart diseases mortality prediction model could acknowledge the patients’ medical professionals that the heart disease risk level of the specific group. This approach is significant in preventing further increases in heart disease mortality rates worldwide. Nowadays, multiple Machine Learning (ML) models, including hybrid models produced impressive predictions and realized that newly developed ML models might provide new perspectives on heart disease predictions. In this paper, we introduced the Facebook Prophet model (FB Prophet model), a time series prediction tool that could present seasonality in its result, since studies point out that heart disease mortality rate also shows seasonality. We produced an accuracy of approximately 94 % in predicting weekly heart disease mortality numbers in specific states. Furthermore, we explored the effects that external factors, ambient temperature, have on heart disease, and utilize this relationship in improving model accuracy.

Multiple Disease Prediction System

Abstract: There are number of hospitals in the world with advanced diagnostic equipment. But although having this equipment some patients cannot get proper treatments and may suffer to death. Main reason behind this is time, our medical systems lack time and it is not easy for them to manage time. With help of machine learning techniques we created project that identifies patients with major diseases like Heart disease, Kidney disease and Diabetes disease at early stage so that proper treatments can be given to them. We collected three datasets for three models from Kaggle [1], analyzed[2]them, cleaned them and choose best algorithm [3] for each dataset. We achieved 98.52% accuracy on heart disease prediction model [4], 98.73% accuracy on kidney disease prediction model [5], 80.55% accuracy on diabetes disease prediction model [6]. For all three models we used Random Forest algorithm [7] .At the end we created web application using Flask [8] for easy user interaction.

An Integrated Accurate-Secure Heart Disease Prediction (IAS) Model using Cryptographic and Machine Learning Methods

An Integrated Accurate-Secure Heart Disease Prediction (IAS) Model using Cryptographic and Machine Learning Methods