The sum of all the lives destroyed every year by natural disasters in different countries is estimated at more than ten thousand. This paper looks at the effects of natural disasters in the world and then examines whether the strategies developed to cope with these calamities are beneficial for the people. In view of the country's wealth, the economy is a serious concern. The paper focuses exclusively on the natural disasters and examines the most recent data. It deals with crop damage, property damage, the deaths of people, and economic losses the country has suffered in recent years. The aim of the paper is to evaluate whether any improvements are needed based on past and current data.

Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments where complex neuroimages are discriminated using ML technology. Radiologists and researchers found that sole decision by the ML algorithms is not accurate enough to implement the treatment procedure. Thus, an intelligent decision is required further by the radiologists after evaluating the ML outcomes. The current research is based on the critical ML, where radiologists’ critical thinking ability, IQ (intelligence quotient), and experience in radiology have been examined to understand how these factors affect the accuracy of neuroimaging discrimination. A primary quantitative survey has been carried out, and the data were analysed in IBM SPSS. The results showed that experience in works has a positive impact on neuroimaging discrimination accuracy. IQ and trained ML are also responsible for improving the accuracy as well. Thus, radiologists with more experience in that field are able to improve the discriminative and diagnostic capability of CML.

The heart attack happens if the flow of blood leads to blocks in any of the blood veins and vessels liable for delivering blood into internal parts of the heart. In the modern life activities and habits, the males and females hold the same responsibility and burden of risk. The absence of understanding frequently leads to a postponement in dealing with the heart attack issues, which could worsen the injury and in most of the situations shown to be dead. Several researchers have applied data mining techniques to diagnose illnesses, and the results have been encouraging. Some methods forecast a specific illness, whereas others predict a wide spectrum of illnesses. In addition, the accuracy of sickness predictions can be improved. This post went into great length on the many approaches of data classification that are currently available. Algorithms primarily represent themselves through representations. Data classification is a typical but computationally intensive task in the area of information technology. A huge amount of data must be analysed in order to come up with an effective plan for fighting disease. Metaheuristics are frequently employed to tackle optimization issues. The accuracy of computing models can be improved by using metaheuristic techniques. Early disease diagnosis, severity evaluation, and prediction are all popular uses for artificial intelligence. For the sake of patients, health care costs, and slowed course of disease, this is a good idea. Machine learning approaches have been used to achieve this. Using machine learning and metaheuristics, this study attempts to classify and forecast human heart disease.

Article Retraction to “A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis” was published on January 1, 2024 in the journal Open Life Sciences (volume 19, issue 1).

Lung cancer is a substantial health issue globally, and it is one of the main causes of mortality. Malignant mesothelioma (MM) is a common kind of lung cancer. The majority of patients with MM have no symptoms. In the diagnosis of any disease, etiology is crucial. MM risk factor detection procedures include positron emission tomography, magnetic resonance imaging, biopsies, X-rays, and blood tests, which are all necessary but costly and intrusive. Researchers primarily concentrated on the investigation of MM risk variables in the study. Mesothelioma symptoms were detected with the help of data from mesothelioma patients. The dataset, however, included both healthy and mesothelioma patients. Classification algorithms for MM illness diagnosis were carried out using computationally efficient data mining techniques. The support vector machine outperformed the multilayer perceptron ensembles (MLPE) neural network (NN) technique, yielding promising findings. With 99.87% classification accuracy achieved using 10-fold cross-validation over 5 runs, SVM is the best classification when contrasted to the MLPE NN, which achieves 99.56% classification accuracy. In addition, SPSS analysis is carried out for this study to collect pertinent and experimental data.