https://dspace.qou.edu/handle/194/2476 Tue, 28 Apr 2026 17:59:08 GMT 2026-04-28T17:59:08Z https://dspace.qou.edu/handle/194/3069 A Hybrid Model for Predicting Heart Disease Using DNN and ML Algorithms Ayad Salamah, Tharaa; Abuzir, Prof. Yousef Cardiovascular diseases are among the most prominent health challenges facing medical systems worldwide, with high mortality rates associated with this disease recorded annually. This is largely due to the difficulty of early detection of these diseases, as diagnosis often relies on clinical findings that appear in advanced stages of the disease. In this context, the need to employ artificial intelligence (AI) techniques, particularly machine learning and deep learning, emerges to develop accurate predictive models that contribute to the early identification of patients at risk of heart disease. This study aims to develop a hybrid model that combines traditional machine learning algorithms—Support Vector Machines (SVMs), Random Forests, XGBoost, LightGBM, and Logistic Regression—with Deep Neural Networks (DNNs), which are used to extract advanced representative features from data. This study uses a reliable medical dataset obtained from Kaggle consisting of four databases: Cleveland, Hungary, Switzerland, and Long Beach. It contains 76 features, including the predicted feature, but all published experiments report using a subset of 14 of them. The target field indicates the presence of heart disease in the patient. It is an integer value where 0 = no disease and 1 = disease. This study introduces an innovative multi-stage methodology for heart disease prediction, leveraging feature augmentation to enhance performance on a moderately sized dataset of 1,025 patient records. The process begins with preprocessing clinical data, comprising 13 features (e.g., age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate, exercise induced angina, oldpeak, slope, number of major vessels, thal ), followed by initial training of traditional machine learning models (e.g., SVM, Random Forest , XGBoost, LightGBM , Logistic Regression) using these features. A Deep Neural Network (DNN) then extracts 32 high-level features from its penultimate layer, capturing complex, nonlinear patterns not evident in the original data. These DNN features are combined with the 13 clinical features to form a 45-feature set, significantly enriching the input space. A set of comprehensive evaluation indicators was used, including accuracy, confusion matrix, precision, recall, F1 coefficient, and the area under the ROC-AUC curve, to provide a comprehensive evaluation of the models before and after feature combination. The Random Forest model achieved the highest performance among classification models on the original features, with an accuracy rate of 97.80%, a high recall rate of 99.31%, and a predictive accuracy of 96.64%. In a previous study, Smith et al. (2018) employed traditional machine learning algorithms The experimental results showed that the Support Vector Machine achieved an accuracy of 85.2%. The results also showed a significant improvement in the performance of all classification algorithms after combining the original features with those extracted by deep neural networks (DNNs). This combination resulted in increased classification accuracy across all key indicators. The SVM algorithm achieved the highest AUC value of 99.90%, demonstrating its high ability to accurately distinguish between classes. The Random Forest, XGBoost, and LightGBM algorithms also achieved identical results in overall accuracy (99.63%) and other indicators. The results showed that combining the DNN-extracted features with the original features led to a significant improvement denotes consistency across models, not the numerical accuracy alone. in the prediction accuracy of all machine learning algorithms used, reflecting the effectiveness of the hybrid approach in enhancing predictive performance, especially in light of the challenges associated with class imbalance and small dataset sizes. This study confirms that combining machine learning and deep learning techniques provides a promising path for developing intelligent diagnostic tools capable of supporting medical decision-making, reducing false alarm rates, and contributing to improving early treatment opportunities, especially in resource-constrained medical settings. Fri, 06 Feb 2026 00:00:00 GMT https://dspace.qou.edu/handle/194/3069 2026-02-06T00:00:00Z https://dspace.qou.edu/handle/194/3068 The Effectiveness of An In-Service Training Program to Develop the Professional Performance of Kindergarten Teachers for Early Detection of Children with Disabilities Marar, Rana; Farah Suhail, Professor: Tamer The study aimed to focus on an effective in-service training program for work performance for creative kindergarten teachers for children from Paris. The quasi-experimental model study was used, with (30) teachers coloring the study, where the measurement group was pre-tested, and a post-measurement on study performance (questionnaire) was prepared. Researcher training program, It was conducted on a sample of (30) female teachers, distributed into two groups, a control group (15) and an experimental group (15), and the results showed that there were statistically significant differences at the level of significance (≥05) between the scores of the experimental group members on the professional performance scale in... The (post) measurement is in favor of the experimental group The results of the study indicated that there were statistically significant differences between the pre- and post-measurements of the professional performance of kindergarten teachers, and the differences were attributed in favor of the post-measurement. Sun, 10 Nov 2024 00:00:00 GMT https://dspace.qou.edu/handle/194/3068 2024-11-10T00:00:00Z https://dspace.qou.edu/handle/194/3067 An Evaluative Study of the Services Provided by the Ministry of Social Development for Children Exposed to Domestic Violence: Perspectives of a Sample of Social Workers in the Northern West Bank Maram Ibrahim Abu Lebda; Dr. Iyad Mohammad Amawi The study aimed to identify the services provided by the Ministry of Social Development to children exposed to domestic violence from the perspective of a sample of social workers in the northern West Bank, It aimed to determine whether there were statistically significant differences according to (governorate, place of work, gender, marital status, Age, Professional experience), and to achieve this end of the study, the researcher used the descriptive approach, where the study tool was designed in the form of a questionnaire, consisting of (60) items, distributed over four axes: counseling and awareness, protection services, social services, and psychological counseling services, The study population consisted of (740) social workers, while the study sample comprised (216) social workers in the northern West Bank governorates. Statistical analysis of the data was performed using the Statistical Package for the Social Sciences (SPSS), The study results indicated that the level of services provided by the Ministry of Social Development was high at 84.1%, with the total score with the total score for protection services at 86.4%, social services at 84.7%, counseling and awareness services at 83.4%, and psychological counseling services at 82%, which was the lowest among the domains. The study also found no statistically significant differences at the significance level (0.05 ≥ a) in the level of services provided by the Ministry of Social Development to children exposed to domestic violence from the perspective of the sample of social workers in the northern West Bank, attributed to the variable of age. However, there were differences in terms of gender, marital status, governorates, workplace, and years of experience. Wed, 07 Feb 2024 00:00:00 GMT https://dspace.qou.edu/handle/194/3067 2024-02-07T00:00:00Z https://dspace.qou.edu/handle/194/3066 Quality of Life of Families of Psychiatric Patients in Mental ‎Health Care Institutions: A Proposed Program from the ‎Perspective of Family Therapy in Social Work to Enhance It Mustafa Shomali, Asala; Fayez Abu Bakr, Dr. Iyad The current study aimed to examine the quality of life among families of psychiatric patients residing in mental health care institutions, and to explore its relationship with a number of demographic and social variables. Additionally, the study sought to propose a program based on the family therapy perspective within social work, aiming to enhance the quality of life of these families, given their critical role in supporting and assisting patients throughout treatment and rehabilitation. The study population consisted of all families of psychiatric patients in mental health care institutions. An exploratory sample of (30) individuals, not included in the main sample, was selected to assess the validity and reliability of the research instrument. The main sample included (100) family members, selected through a convenience sampling method. The results indicated that the overall mean score of the study sample on the Quality of Life Scale was (3.52), representing a percentage of (70.4%), which reflects a moderate level of quality of life among families accompanying patients. The mean scores of the quality of life domains ranged between (3.20–3.77), with the health and service domain ranking first (M = 3.77, 75.4%) and rated as high, while the educational and cognitive domain ranked last (M = 3.20, 64.0%) and rated as moderate. The findings also revealed variations in the level of quality of life across different domains, with no statistically significant differences attributed to variables such as age, relationship to the patient, or duration of caregiving. However, statistically significant differences were found in the economic and living domain attributed to educational level, in favor of families with higher academic qualifications (Master’s degree or above). Additionally, significant differences appeared in the psychological and emotional, economic and living, health and service, and educational and cognitive domains according to the family’s average monthly income, favoring families with higher income levels. Based on these findings, the study recommends developing targeted psychological and social support programs for the families of psychiatric patients and strengthening the role of family therapy within the professional framework of social work practice. Tue, 16 Dec 2025 00:00:00 GMT https://dspace.qou.edu/handle/194/3066 2025-12-16T00:00:00Z