The history of data stored can be used to forecast potential patterns and help companies make competitive decisions to increase their success and benefits. Many analysts look at healthcare sector data to identify and forecast illnesses in order to benefit patients and physicians in a variety of ways. This study is concerned with the diagnosis and estimation of heart disease. Heart disease is one of the most dangerous illnesses for humans, leading to death all over the world. Many different groups of researchers have used knowledge exploration methods in diverse fields to forecast heart disease and have shown acceptable degrees of precision. There were no real-time methods for analyzing and forecasting heart disease in its early stages. For the prediction of heart disease, decision trees are used to analyze various training and evaluation datasets. Classification algorithms such as Naive Bayes, ID3, C4.5, and SVM are being investigated. The UCI machinery heart disease data set is used in experimental studies.

Water contamination is a pervasive global crisis, affecting over 2 billion people worldwide, with pharmaceutical contaminants emerging as a significant concern due to their persistence and mobility in aquatic ecosystems. This review explores the potential of activated hydrochars, sustainable materials produced through biomass pyrolysis, to revolutionize the removal of pharmaceutical contaminants from water sources. These materials possess high surface area, porous structure, and exceptional adsorption capabilities, making them a promising solution. The impact of pharmaceutical contaminants on aquatic ecosystems and human health is far-reaching, affecting biodiversity, water quality, and public health. To address this complex issue, a diverse range of techniques, including adsorption, biodegradation, and advanced oxidation processes, are employed in the pharmaceutical industry. Activated hydrochars offer substantial adsorption capacity, sustainable feedstock origins, and a minimal carbon footprint. This review highlights their potential in pharmaceutical contaminant removal and their broader applications in improving soil and air quality, resource recovery, and sustainable waste management. Interdisciplinary collaboration and the development of intelligent treatment systems are essential to fully unlock the potential of activated hydrochars. Regulatory support and policy frameworks will facilitate their responsible and widespread application, promising a cleaner and more sustainable future. This paper aims to inform scientists, environmental experts, policymakers, and industry stakeholders about the promising role of activated hydrochars in addressing pharmaceutical contaminant challenges.

“Malignant mesothelioma (MM)” is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is “malignant mesothelioma.” Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination “epithelial/mesenchymal appearance of MM,” however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful “Malignant Mesothelioma illness” identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support “Vector Machine, Neural Network, and Decision Tree” are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.