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PurposeHeart failure is a complex clinical condition when the heart cannot provide blood with enough flow for the body's needs. It is a major clinical and public health problem. Even if heart failure is not yet diagnosed, it is important to get your health checked every three to six months. This study aims to improve the accuracy of diagnosing heart failure by using machine learning classifiers such as Recursive Feature Elimination (RFE) and Synthetic Minority Oversampling Technique (SMOTE). MethodsHeart failure data has been acquired from the University of California, Irvine (UCI) repository. To improve the accuracy of diagnosing heart failure, we employed the following methods for this study: k-Nearest Neighbor, Naive Bayes (NB), Random Forest, XGBoost, Decision Tree (DT), Logistic Regression (LR), and Support Vector Machines (SVM). The model was validated using the F-measure and ROC-AUC (Receiver Characteristic Area Under Curve) methods. ResultsSupport vector machines employing logistic regression as a feature selection strategy produced the most significant classification accuracy of 90%, while support vector machines utilising RF as a feature selection strategy showed an accuracy of 83%. We also have an accuracy of 90% in the random forest as a machine learning methodology with all of our features.ConclusionThe small dataset size of the current research presents a challenge to everyone's ability to gain more accurate findings. Improved diagnostics for heart failure may be possible in the future using our machine-learning classifier-based classification system. To accurately forecast heart failure, this is the easiest way to use and the most accurate.

In this article, a new extension of the one parameter Xgamma distribution has been proposed. Also the associated different statistical properties are derived. The unknown parameter of the proposed distribution is estimated by using different classical estimation methods and by using Bayesian estimation method. Under classical methods of estimation, we brieflfly describe the method of moment estimators, maximum likelihood estimators, maximum product of spacing estimators, least squares and weighted least squares estimators and Cramer-von-Mises estimators. The Bayesian estimation using gamma prior under squared error loss function has been discussed and computed via Lindley’s approximation and Markov Chain Monte Carlo techniques. Furthermore, the 100(1 − α)% asymptotic confifidence interval and credible interval along with the coverage probability are also discussed. The obtained classical and the Bayesian estimators are compared through Monte Carlo simulations. Next, we construct a modifified Chi-squared goodness of fifit test based on the Nikulin-Rao-Robson (NRR) statistic in presence of censored and complete data. The applicability of our proposed model has been illustrated for both complete data and right censored data by using two real data sets for each.

In this article, a new flexible extension of xgamma probability distribution has been proposed. Several well known distributional properties viz., raw moments, generating functions, conditional moments, mean deviation, quantile functions etc., of this flexible extension model have derived and studied in detail. Further, the estimation of the unknown model parameters along with the survival function and hazard function are estimated using maximum likelihood estimation technique. The Monte Carlo simulation has been performed to check the consistency of the proposed estimators for the different variation of sample size and model parameters. Finally, the superiority of proposed extension over several well known lifetime models has been illustrated using four data sets pertaining to COVID-19 cases in different country of the world.

In this article, a reliability test plan is developed for Logistic-exponential distribution (LoED) under time truncated life test scheme. The distribution has been chosen because it can used to model lifetime of several reliability phenomenon and it performs better than many well known existing distributions. With the discussions of statistical properties of the aforesaid model, the reliability test plan has been established under the assumption of median quality characteristics when minimum confidence level P* is given. To quench the objective of the paper i.e; to serve as a guiding aid to the emerging practitioners, minimum sample sizes have been obtained by using binomial approximation and Poisson approximation for the proposed plan. Further, operating characteristic (OC) values for the various choices of quality level are placed. Also, minimum ratio of true median life to specified life has been presented for specified producer’s risk. Important findings of the proposed reliability test plan are given for considered value of k=0.75,1,2. To demonstrate the appropriateness of suggested reliability test plan is achieved using four real life situation.