This study explores the application of machine learning in the prediction of stroke occurrences, a critical task in healthcare with the potential to save lives and reduce the impact of this life-altering medical event. Leveraging the "Healthcare Stroke Data" dataset, we employed two powerful classification models, the Random Forest and Support Vector Machine (SVM), to forecast stroke likelihood. Our analysis encompasses data preprocessing, model training, and comprehensive evaluation using classification metrics and confusion matrices. The study reveals the trade-offs between accuracy, recall, precision, and the F1 score in both models. While the Random Forest exhibits higher accuracy, the SVM excels in recall, a crucial factor in healthcare. Precision challenges in both models highlight the need for further refinement. Additionally, we conducted a feature importance analysis, emphasizing the pivotal role of age, BMI, and glucose levels in stroke prediction. This work exemplifies the potential of machine learning in healthcare and contributes to ongoing efforts in improving stroke prediction and prevention.

This study delves into the application of machine learning models for the early detection of Autism Spectrum Disorder (ASD). Early diagnosis and intervention are critical for improving the lives of individuals with ASD and their families. This research compares various machine learning models, including Decision Tree, Random Forest, Support Vector Machine, k-Nearest Neighbors, and more, assessing their performance based on key metrics such as F1-Score, accuracy, precision, and recall. The study reveals the Multi-layer Perceptron (MLP) as the top-performing model with an impressive F1-Score of 79.35%, demonstrating its potential for accurate ASD detection. The feature importance analysis highlights the significant roles of gender, genetic predisposition, age at diagnosis, and ethnicity-related features in predicting ASD. This study underscores the promise of machine learning in ASD detection and emphasizes the importance of early intervention and personalized approaches to diagnosis.

Abstract In this work, the mutual solubilities of sets of ionic liquids ([CnMIM] [TF2N] (n = 4, 8, 12), [C4PY] [TF2N], [C8MIM] [OTF]) and organic compounds (heptane, o-xylene, toluene, or water) are investigated. The experimental data measured for these systems were used to adjust the binary interaction parameters between their components for the Non-Random Two Liquid (NRTL) model. The results showed that the solubility increased with temperature, with high hygroscopicity (10-1 in terms of mole fraction) of the ILs, low interactions with aliphatic hydrocarbons, high interactions with aromatic hydrocarbons and the presence of a lower critical solution temperature (LCST). In addition, this study is the first to show that [C12MIM] [TF2N] is completely soluble in toluene and ortho-xylene between 273.15 and 373.15 K at 1 bar. The average deviations related to the mole fraction between the experimental and calculated values by the NRTL were less than 2.4%.