http://repository.iphce.org/xmlui/handle/123456789/4950 Wed, 20 May 2026 18:18:26 GMT 2026-05-20T18:18:26Z http://repository.iphce.org/xmlui/handle/123456789/5158 Prediction of childhood Depression using machine learning Technique Abera, Samrawit Childhood depression is a critical public health issue that often goes undiagnosed due to subtle symptoms and limited awareness. This research proposes a machine learning and deep learning based system to predict childhood depression using supervised learning algorithms and neural network architectures such as Multi-Layer Perceptron’s (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) networks. The study addresses challenges such as the lack of organized datasets and the timeconsuming process of digitizing paper-based records. Feature selection techniques were utilized to identify the most predictive attributes, while comparative analysis of models ensured the selection of the most effective approach. Blockchain technology is suggested as an enhancement to improve data security and transparency, enabling professionals and guardians to monitor mental health status seamlessly. The study stresses the importance of incorporating real-time datasets to advance the model's accuracy and responsiveness. The results show that, while promising accuracy was achieved, future research should explore additional features and larger, more diverse datasets to further improve performance. This system aims to assist mental health professionals in making timely, data-driven decisions and contribute to the early identification and management of childhood depression. Sat, 01 Feb 2025 00:00:00 GMT http://repository.iphce.org/xmlui/handle/123456789/5158 2025-02-01T00:00:00Z http://repository.iphce.org/xmlui/handle/123456789/5157 HIV Target Group Prediction Using Machine Learning Abebual, Yosef HIV continues to be a global health concern that necessitates cutting-edge methods of diagnosis and treatment. Owing to the intricate nature of the HIV pandemic, specific strategies are needed to pinpoint vulnerable people. This study tackles the challenge of precise identification within specific HIV target groups, namely Adolescent Girls and Young Women (AGYW), High-Risk Men (HRM), and Female Sex Workers (FSW). Leveraging machine learning algorithms include Support vector machine, XGBoost, Random forest and linear regression. The research integrates locally sourced datasets from hospital records, aiming to elevate intervention precision. The study seeks to transform public health by introducing a data-driven approach to unravel intricate relationships and variables influencing HIV prevalence among distinct target groups. Despite progress in global health efforts, traditional methods grapple with precision and efficiency limitations. The adoption of machine learning offers a promising solution, contributing to a nuanced understanding of dynamics within key populations. Addressing gaps in existing literature particularly the scarcity of studies at the intersection of machine learning and the identification of specific HIV target groups using locally collected datasets. The study rigorously evaluates the performance of four algorithms on an HIV service delivery dataset. Results indicate consistently high accuracy across all models, with ensemble approaches (XGBoost and Random Forest) slightly outperforming others. Notably, Support Vector Machine achieved 96.33% accuracy, XGBoost reached 96.51%, Random Forest attained 96.49%, and Linear Regression demonstrated commendable accuracy at 96.28%. This research significantly contributes to advancing machine learning applications in healthcare and addresses a crucial gap in the current body of knowledge. Sat, 01 Jun 2024 00:00:00 GMT http://repository.iphce.org/xmlui/handle/123456789/5157 2024-06-01T00:00:00Z http://repository.iphce.org/xmlui/handle/123456789/5156 The causes and effects of obstetric fistula on patients in Hamlin Fistula Hospital in Addis Ababa, Ethiopia Solomon, Natnael This research paper investigated the causes and effects of obstetric fistula on patients in Hamlin fistula hospital. The objective of this study was to examine the cause and effects of obstetric fistula on patients. The effects investigated include the physical health, social and psychological effects of obstetric fistula. The study was undertaken utilizing in depth interview as a key instrument in data collection.The participants interviewed for this study include 10 obstetric fistula patients in Hamlin and two key informants namely psychiatric nurse and head nurse of the stomach clinic department. Methodologically the research study used qualitative research approach and case study as a research design. The data obtained from participants were analyzed using thematic analysis with transcription and coding being done before hand. The key findings of the research study are: first life events of participant’s especially traumatic life events, local rural traditions, prolonged labor and assistance by traditional midwifer has played an important role in causing their obstetric fistula. Second the physical health problems experienced by the participants differ from person to person depending on the damage they incurred during birth and the amount of time they took to get treatment. Third the social part of participant’s lives was severely affected because of the smell associated with obstetric fistula patients leading to self-isolation or discrimination by society or family members. Fourth the psychological effects of obstetric fistula differ from person to person and its severity is conditioned on how much support the participants either got from their families or communities or how soon they got treatment or counseling from professional mental health professional. Obstetric fistula is a complex condition that affects patients in different ways and different level of severity depending on different social and medical factors. This research study recommends an increase in awareness campaign on the causes and effects of obstetric fistula especially in rural part of Ethiopia and also intervention plan that addresses the multiple problems of obstetric fistula patients. The intervention should also include family members of obstetric fistula patients in order to address the problem in a holistic manner. Mon, 01 Jul 2024 00:00:00 GMT http://repository.iphce.org/xmlui/handle/123456789/5156 2024-07-01T00:00:00Z http://repository.iphce.org/xmlui/handle/123456789/5155 The effect of reward management practices on employees turnover intention in the case of abebe bikila health center Adugna, Meron Human capital is a paramount important part of todays’ business world. For any company to achieve its corporate strategies, it is important to have motivated, committed workforce within the company. Losing experienced human resource by voluntary turnover is very costly for the organization and difficult to recover from when it specially occurs in large quantity. Regardless of the huge negative impact of turnover intention on the goal achievement of organizations, serious attention and research to address the problem specially in the health sector is very limited in many organizations in Ethiopia including Abebe Bikila health center.The study considered various independent variables including remuneration, cash incentives, promotion opportunities, recognition, and work conditions. The research aimed to determine how these factors influence the likelihood of employees leaving their current job. Based on quantitative survey, the data was collected from 202 sample group that are working in the center at clerical, non-clerical ,supervisory and health professional levels. By implementing the proper statistical tests, the study analysis uncovered that the first three out of the five candidate variables (remuneration,cash incentives , promotion ,recognition and work condition) were found to strongly correlate well with turnover intention. By investigating the relationship between reward management practices and turnover intentions, this study provided valuable insights for organizations specially healthcare centers looking to reduce employee turnover and improve retention strategies. Mon, 01 Jul 2024 00:00:00 GMT http://repository.iphce.org/xmlui/handle/123456789/5155 2024-07-01T00:00:00Z