Prediction of drinking water quality with machine learning models: A public health nursing approach.

OBJECTIVE: The aim of this study is to use machine learning models to predict drinking water quality from a public health nursing approach. DESIGN: Machine learning study. SAMPLE: "Water Quality Dataset" was used in the study. The dataset contains physical and chemical measurements of water quality for 2400 different water bodies. The process consists of four stages: Data processing with Synthetic Minority Oversampling Technique, hyperparameter tuning with 10-fold cross-validation, modeling and comparative analysis. 80% of the dataset is allocated as training data and 20% as test data. ML models logistic regression, K-nearest neighbor, support vector machine, random forest, XGBoost, AdaBoost Classifier, Decision Tree algorithms were used for water quality prediction. Accuracy, precision, recall, F1 score and AUC performance metrics of ML models were compared. To evaluate the performance of the models, 10-fold cross-validation was used and a comparative analysis was performed. The p-values of the models were also compared. RESULTS: N this study, where drinking water quality was predicted with seven different ML algorithms, it can be said that XGBoost and Random Forest are the best classification models in all performance metrics. There is a significant difference in all ML algorithms according to the p-value. The H0 hypothesis is accepted for these algorithms. According to the H0 hypothesis, there is no difference between actual values and predicted values. CONCLUSION: In conclusion, the use of ML models in the prediction of drinking water quality can help nurses greatly improve access to clean water, a human right, be more knowledgeable about water quality, and protect the health of individuals.

Web search behaviors and infodemic attitudes regarding COVID-19 in Turkey: A framework study for improving response and informing policy on the COVID-19 infodemic.

Objective: This study aimed to develop a framework regarding COVID-19 infodemic response and policy informing through focusing on infodemic concepts circulating on the online search engine in Turkey in relation to the COVID-19 outbreak and comparing the contents of these concepts with Maslow's hierarchy of needs and disaster stages. Materials and methods: The universe of this descriptive epidemiological research consists of internet search activities on COVID-19 circulating online on Google Trends between March 10, 2020, when the first case was seen in Turkey, and June 01, 2020, when the lockdown restrictions were lifted. Findings: There was no internet trend regarding a misinformed attitude within the given date range. While an infodemic attitude toward superficial attitude and racist attitude in the internet environment was detected for 1 week, an infodemic attitude toward definitive attitude was detected for 2 weeks. The non-infodemic concepts were more common than the other infodemic attitudes. The infodemic concepts were able to reach Maslow's physiological, safety, and social need levels. With the infodemic concepts obtained, a COVID-19 development process framework was developed. The framework consists of three domains (COVID-19, applications and outcomes), including disaster phases and health/social impacts, built on seven public health epochs. Results: A systematized COVID-19 development process framework was modeled in order to conceptualize COVID-19 internet searches and to reveal the development processes and outcomes.

Discussions About COVID-19 Vaccination on Twitter in Turkey: Sentiment Analysis.

OBJECTIVES: The present study aims to examine coronavirus disease 2019 (COVID-19) vaccination discussions on Twitter in Turkey and conduct sentiment analysis. METHODS: The current study performed sentiment analysis of Twitter data with the artificial intelligence (AI) Natural Language Processing (NLP) method. The tweets were retrieved retrospectively from March 10, 2020, when the first COVID-19 case was seen in Turkey, to April 18, 2022. A total of 10,308 tweets accessed. The data were filtered before analysis due to excessive noise. First, the text is tokenized. Many steps were applied in normalizing texts. Tweets about the COVID-19 vaccines were classified according to basic emotion categories using sentiment analysis. The resulting dataset was used for training and testing ML (ML) classifiers. RESULTS: It was determined that 7.50% of the tweeters had positive, 0.59% negative, and 91.91% neutral opinions about the COVID-19 vaccination. When the accuracy values of the ML algorithms used in this study were examined, it was seen that the XGBoost (XGB) algorithm had higher scores. CONCLUSIONS: Three of 4 tweets consist of negative and neutral emotions. The responsibility of professional chambers and the public is essential in transforming these neutral and negative feelings into positive ones.

Vaccination coverage and related factors among the elderly: A cross-sectional study from Turkey.

OBJECTIVE: The study was conducted to determine the vaccination rates and related factors among the elderly. DESIGN: Cross-sectional study. SAMPLE: This study was conducted with 984 elderly people living in a province in western Turkey. MEASUREMENTS: The single-stage cluster sampling method was used in the sample selection. The descriptive statistics, the chi-square analysis, the Mann-Whitney U test and the logistic regression analysis for the multivariate analysis were used to evaluate the data. RESULTS: It was determined that 45.6% of the elderly were vaccinated after the age of 65 and the most frequently administered vaccines were influenza (41.3%), pneumococcal (10.9%), and tetanus (5.5%) vaccines. Higher vaccination rates were determined in the following demographics, namely by 1.8-fold (95% CI, 1.4-2.4) in those living in urban areas, by 2.6-fold (95% CI, 1.8-3.9) in those with high school or higher education, by 1.5-fold (95% CI, 1.0-2.5) in those who did not work, by 1.7-fold (95% CI, 1.3-2.3) in those with chronic diseases and by 2-fold (95% CI, 1.1-3.4) in those who fulfilled their physical own needs themselves. CONCLUSION: This study showed that more than half of the elderly did not receive any vaccinations in old age. The vaccination rates of the elderly were associated with many factors.