Prevalence and factors affecting the utilization of antenatal care in rural areas of Southwestern Ethiopia.

BACKGROUND: Antenatal care (ANC) is a health care intervention intended to ensure the safety of pregnancy. According to the World Health Organization, at least four ANC visits are recommended for a healthy pregnancy. However, whether this recommended number of visits was followed or not in the rural areas of Southwestern Ethiopia is not known. Therefore, the study aimed to investigate the prevalence of, and the associated factors of ANC utilization by pregnant women in the rural areas of Southwestern Ethiopia. METHODS: A community-based cross-sectional study design was used in three rural zones. The data were collected from n = 978 women through a structured questionnaire with face-to-face interview. The collected data were analyzed using descriptive statistics and a multiple binary logistic regression model. RESULTS: The results showed that 56% of women made the recommended minimum number of ANC visits and the remaining 44% of them underutilized the ANC service. The multiple binary logistic regression model identified zone, marital status of the woman, educational level of the husband, occupation of the husband, knowledge of danger signs of pregnancy, birth interval, source of information, timely visits, and transportation problem to be statistically significant factors affecting the prevalence of ANC visit utilization of women. Bench Maji zone had smaller odds ratio of ANC visit prevalence as compared to Kaffa zone. Women who lived in the rural area of Sheko zone are 2.67 times less likely to utilize ANC visit than those who lived in the rural area of Kaffa zone keeping other variables constant. CONCLUSION: The study results highlight the need to increase the number of ANC visits, and the importance of using an appropriate model to determine the important socio-demographic factors that ANC service providers shall focus on to improve the health of the unborn baby and the mother during pregnancy.

Hand Gesture of Recognition Pattern Analysis by Image Treatment Techniques.

The goal of this project is to write a program in the C++ language that can recognize motions made by a subject in front of a camera. To do this, in the first place, a sequence of distance images has been obtained using a depth camera. Later, these images are processed through a series of blocks into which the program has been divided; each of them will yield a numerical or logical result, which will be used later by the following blocks. The blocks into which the program has been divided are three; the first detects the subject's hands, the second detects if there has been movement (and therefore a gesture has been made), and the last detects the type of gesture that has been made accomplished. On the other hand, it intends to present to the reader three unique techniques for acquiring 3D images: stereovision, structured light, and flight time, in addition to exposing some of the most used techniques in image processing, such as morphology and segmentation.

The Commitment of Nineveh Governorate Residents to the Precautionary Measures against Global 2019 Pandemic and Dermatological Affection of Precautions.

The world has changed dramatically since the novel pandemic pours in all aspects of life, including economic, health, and social life. The first case was initially observed in the Wuhan province of China; fast spread occurs around the world. Until now, there is no proven effective treatment for it. The study's objectives are to assess residents of Nineveh governorate's commitment to the COVID-19 pandemic precautionary measures recommended by the WHO and Iraqi national authorities; the protective measures are used to prevent its spread and restrict the viral infectivity. Several cutaneous changes were observed in some persons as a result of prolonged contact with personal protective equipment and excessive use of personal hygiene measures.

Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing.

Heart disease is the leading cause of death from chronic diseases in the developing countries. The difficulty of making an accurate and timely diagnosis is exacerbated by a lack of resources and professionals in some areas, which contributes to this reality. Medical professionals may benefit from technological advancements that aid in the accurate diagnosis of patients. In light of these findings, a hybrid diagnostic tool has been developed that combines several computational intelligence (machine learning) techniques capable of analyzing clinical histories and images of electrocardiogram signals and indicating whether or not the patient has ischemic heart disease with up to 97.01% accuracy. Working with medical experts and a database containing clinical data on approximately 1020 patients and their diagnoses was required for this project. Both were put to use. A picture database containing 92 images of electrocardiogram signals was also used in this project for the analysis of the Artificial Neural Network. After extensive research and testing by the medical community, which supported the project and provided positive feedback, a successful tool was developed. This demonstrated the tool's effectiveness.

Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives.

Chronic kidney disease (CKD) is a global health issue with a high rate of morbidity and mortality and a high rate of disease progression. Because there are no visible symptoms in the early stages of CKD, patients frequently go unnoticed. The early detection of CKD allows patients to receive timely treatment, slowing the disease's progression. Due to its rapid recognition performance and accuracy, machine learning models can effectively assist physicians in achieving this goal. We propose a machine learning methodology for the CKD diagnosis in this paper. This information was completely anonymized. As a reference, the CRISP-DM® model (Cross industry standard process for data mining) was used. The data were processed in its entirety in the cloud on the Azure platform, where the sample data was unbalanced. Then the processes for exploration and analysis were carried out. According to what we have learned, the data were balanced using the SMOTE technique. Four matching algorithms were used after the data balancing was completed successfully. Artificial intelligence (AI) (logistic regression, decision forest, neural network, and jungle of decisions). The decision forest outperformed the other machine learning models with a score of 92%, indicating that the approach used in this study provides a good baseline for solutions in the production.