Predicting cardiac disease is considered one of the most challenging tasks in the medical field. It takes a lot of time and effort to figure out what’s causing this, especially for doctors and other medical experts. In this paper, various Machine Learning algorithms such as LR, KNN, SVM, and GBC, together with the GridSearchCV, predict cardiac disease. The system uses a 5-fold cross-validation technique for verification. A comparative study is given for these four methodologies. The Datasets for both Cleveland, Hungary, Switzerland, and Long Beach V and UCI Kaggle are used to analyze the models’ performance. It is found in the analysis that the Extreme Gradient Boosting Classifier with GridSearchCV gives the highest and nearly comparable testing and training accuracies as 100% and 99.03% for both the datasets (Hungary, Switzerland &; Long Beach V and UCI Kaggle). Moreover, it is found in the analysis that XGBoost Classifier without GridSearchCV gives the highest and nearly comparable testing and training accuracies as 98.05% and 100% for both the datasets (Hungary, Switzerland & Long Beach V and UCI Kaggle). Furthermore, the analytical results of the proposed technique are compared with previous heart disease prediction studies. It is evident that amongst the proposed approach, the Extreme Gradient Boosting Classifier with GridSearchCV is producing the best hyperparameter for testing accuracy. The primary aim of this paper is to develop a unique model-creation technique for solving real-world problems.

Predicting heart disease is regarded as one of the most difficult challenges in the health-care profession. To predict cardiac disease, researchers employed a variety of algorithms including LDA, RF, GBC, DT, SVM, and KNN, as well as the feature selection algorithm SFS. For verification, the system employs the K-fold cross-validation approach. These six strategies were used to conduct the comparative study. The Dataset for Cleveland, Hungry, Switzerland, and Long Beach V, as well as the Dataset Heart statlog Cleveland hungry, are used to assess the models’ performance. For both hungry, switzerland & Long Beach V and Heart statlog cleveland hungry Dataset, Random Forest Classifier sfs and Decision Tree Classifier sfs produced the highest and almost identical accuracy values (100%, 99.40% and 100%, 99.76 %, respectively). The findings were compared to previous research that focused on cardiac prediction. In the future, we hope to extend the model even further so that it may be used with various feature selection techniques; another possibility is to use a random forest classifier. The major goal of this study is to improve on previous work by developing a new and unique technique for creating the model, as well as to make the model relevant and easy to use in real-world situations.

One of the most difficult challenges in medicine is predicting heart disease at an early stage. In this study, six machine learning (ML) algorithms, viz., logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used to analyze two heart disease datasets. One dataset was UCI Kaggle Cleveland and the other was the comprehensive UCI Kaggle Cleveland, Hungary, Switzerland, and Long Beach V. The performance results of the machine learning techniques were obtained. The support vector machine with tuned hyperparameters achieved the highest testing accuracy of 87.91% for dataset-I and the extreme gradient boosting classifier with tuned hyperparameters achieved the highest testing accuracy of 99.03% for the comprehensive dataset-II. The novelty of this work was the use of grid search cross-validation to enhance the performance in the form of training and testing. The ideal parameters for predicting heart disease were identified through experimental results. Comparative studies were also carried out with the existing studies focusing on the prediction of heart disease, where the approach used in this work significantly outperformed their results.

Heart disease is a danger to people’s health because of its prevalence and high mortality risk. Predicting cardiac disease early using a few simple physical indications collected from a routine physical examination has become difficult. Clinically, it is critical and sensitive for the signs of heart disease for accurate forecasts and concrete steps for future diagnosis. The manual analysis and prediction of a massive volume of data are challenging and time-consuming. In this paper, a unique heart disease prediction model is proposed to predict heart disease correctly and rapidly using a variety of bodily signs. A heart disease prediction algorithm based on the analysis of the predictive models’ classification performance on combined datasets and the train-test split technique is presented. Finally, the proposed technique’s training results are compared with the previous works. For the Cleveland, Switzerland, Hungarian, and Long Beach VA heart disease datasets, accuracy, precision, recall, F1-score, and ROC-AUC curves are used as the performance indicators. The analytical outcomes for Random Forest Classifiers (RFC) of the combined heart disease datasets are F1-score 100%, accuracy 100%, precision 100%, recall 100%, and the ROC-AUC 100%. The Decision Tree Classifiers for pooled heart disease datasets are F1-score 100%, accuracy 98.80%, precision 98%, recall 99%, ROC-AUC 99%, and for RFC and Gradient Boosting Classifiers (GBC), the ROC-AUC gives 100% performance. The performances of the machine learning algorithms are improved by using five-fold cross validation. Again, the Stacking CV Classifier is also used to improve the performances of the individual machine learning algorithms by combining two and three techniques together. In this paper, several reduction methods are incorporated. It is found that the accuracy of the RFC classification algorithm is high. Moreover, the developed method is efficient and reliable for predicting heart disease.

Non-linear programming problems can be solved efficiently by geometric programming in which non-linear expressions can be expressed by an exponential or power function as given in (16). In this paper, Double Sampling Stratification (DSS) strategy is used in multivariate stratified population with unidentified strata weights, for optimum sampling design (OSD) in the presence of non-response to estimate the unidentified population means. The double sampling problem in the presence of non-response is formulated as Fuzzy Multiobjective Convex Programming Problem (FMOCPP). Then, we convert FMOCPP into Fuzzy Single Objective Convex Problem (FSOCP) by using membership function. Dual solution is obtained using LINGO [1] Software by solving FSOCP. The optimum allocations of sample sizes of respondents and non-respondents for both the phases are obtained by applying dual solutions and primal-dual relationship theorem. The given technique is illustrated by projecting a numerical problem.