METHOD: A descriptive cross-sectional study was conducted among the general population of Pakistan to investigate the knowledge and perception about hand hygiene, self-reported hand hygiene practices, adherence to hand hygienic guidelines, and barriers to optimal hand hygiene. Kruskal-Wallis test, Mann-Whitney U test, and Regression model were used for statistical analysis.

RESULTS: There was a significant difference in area-based knowledge (P = 0.026), beliefs (P = 0.027), and practices (P = 0.002) regarding hand hygiene. The results of regression analysis revealed that people in urban areas were more likely to have better knowledge (β = 0.108, CI = 0.076 - 0.05, P = 0.008) and better adherence (β = 0.115, CI = 0.514 - 2.68, P = 0.004) to hand hygienic practices.

RESULT: We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, SVM, and Deep Neural Network algorithms to ASD screening dataset and compared the classifiers' based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic, area under the curve, and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn may impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset.