Background Kruskal-Wallis H test from the bank of classical statistics tests is a well-known nonparametric alternative to a one-way analysis of variance. The test is extensively used in decision-making problems where one has to compare the equality of several means when the observations are in exact form. The test is helpless when the data is in an interval form and has some indeterminacy. Methods The interval-valued data often contain uncertainty and imprecision and often arise from situations that contain vagueness and ambiguity. In this research, a modified form of the Kruskal-Wallis H test has been proposed for indeterminacy data. A comprehensive theoretical methodology with an application and implementation of the test has been proposed in the research. Results The proposed test is applied on a Covid-19 data set for application purposes. The study results suggested that the proposed modified Kruskal-Wallis H test is more suitable in interval-valued data situations. The application of this new neutrosophic Kruskal-Wallis test on the Covid-19 data set showed that the proposed test provides more relevant and adequate results. The data representing the daily ICU occupancy by the Covid-19 patients were recorded for both determinate and indeterminate parts. The existing nonparametric Kruskal-Wallis H test under Classical Statistics would have given misleading results. The proposed test showed that at a 1% level of significance, there is a statistically significant difference among the average daily ICU occupancy by corona-positive patients of different age groups. Conclusions The findings of the results suggested that our proposed modified form of the Kruskal-Wallis is appropriate in place of the classical form of the test in the presence of the neutrosophic environment.

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The implementation of state-of-the-art machine learning (ML) procedures for handling high dimensionality is prolonged in health care, particularly in genetics. Microarray datasets occupying a significant place in genetics are facing the problem of high dimensionality: small sample size but a large number of variables (genes). Therefore, a need is to identify only the significant genes from these large sizes data sets, which are playing a momentous role in the progress of sickness. Metaheuristics, another emerging field for researchers is exploited for the solution of the previously said task. Therefore, the said task is resolved by a newly proposed hybrid which is a combination of two: Particle swarm and genetic algorithm i.e. PSO-GA. Through the usage of the afresh proposed hybrid, the significant genes are designated from the large magnitude data sets. The effectiveness is justified by cooperating with the benchmark unconstraint tests. Thereafter, with the help of the assorted genes, classification of the various gene datasets is done. It is worth noting that the projected hybrid successfully gained its position while classifying several datasets in terms of maximum accuracy. The superlative assortment of associated genes by PSO-GA through an ML classifier has contributed positively to the classification of microarray datasets.

Aim: To foresee the outcome of heart failure(HF) in Pakistani patients with potential predictors and through various machine learning (ML) methods. Study design:The secondary data of Pakistani patients is taken from the UCI repository in which a cross-sectional, analytical study was planned. Place and duration: This data was collected in April-December, 2015 at the Institute of Cardiology and Allied hospital Faisalabad-Pakistan. Methodology: The data set consisted of299 patients distributed among male (194) and female patients (105). Ages, serum sodium (SS), serum creatinine (SC), gender, smoking, high blood pressure (HBP), ejection fraction (EF), anemia, platelets, Creatinine Phosphokinase (CPK), and diabetes were considered as the potential predictors for predicting the outcome of HF.The data set was analyzed with the help of various machine learning (ML) predictive models including Logistic regression (LR), K-nearest neighbor (KNN), and Decision trees (DT). Results: The ages of the patients were within 60.833±11.894 years. Out of 299 patients, 129 were anemic, 105 had high blood pressure (HBP), and 96 had a smoking history. A statistical model was estimated by applying LR which assisted us in identifying the significant predictors. The sensitivity of the LRwas observed to be 92.1%, whereas 85.6% of the outcome of HF patients was correctly predicted by this model (LR) and DT achieved89.6% prediction accuracy. Conclusion:Since HF is a substantial reason for deaths in Pakistan. Therefore, the identification of its potential risk factors and its accurate prediction by some modern tools are highly demanded. This study applied ML tools for the said task and concluded that among all the fitted ML models, DT predicted the correct outcome for HF patients proficiently. Keywords: Heart failure, machine learning, logistic regression, k-nearest neighbor, decision trees