Browsing by Author "Abdullah-Al-Kafi, Md"

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Improving Predictive Analytics for Student Dropout: A Comprehensive Analysis and Model Evaluation
(2024-04-18) Sabbir, Wahid; Abdullah-Al-Kafi, Md; Afridi, Arafat Sahin; Sadekur Rahman, Md.; Karmakar, Mousumi
This research project uses careful data preparation and machine learning model assessment to provide an in-depth analysis of a dataset of students in college or university. The first analysis looks at goal value distributions, economic variables, and student counts by gender. The handling of outliers, feature selection, and class imbalance are all addressed by further filtering. Using ROC curves to highlight classification strength, the study assesses several classifiers, including XGBoost, Random Forest, K-Nearest Neighbors (KNN), and Decision Tree. With the greatest AUC of 0.99, Random Forest remarkably shows excellent predictive power, closely followed by XGBoost at 0.98. XGBoost performs exceptionally well on testing and training datasets. The findings contribute valuable insights into predictive modeling for understanding and predicting student outcomes, emphasizing the potential to enhance educational support systems. This integrated approach, combining exploratory data analysis and machine learning techniques, establishes a robust framework for future research in educational data mining and predictive analytics.
Pre-COVID-19 Knowledge, Attitude and Practice Among Nurses Towards Infection Prevention and Control in Bangladesh: A Hospital-Based Cross-Sectional Survey
(Scopus, 22-12-01) Harun, Md. Golam Dostogir; Anwar, Md Mahabub Ul; Sumon, Shariful Amin; Abdullah-Al-Kafi, Md; Datta, Kusum; Haque, Md. Imdadul; Chowdhury, A. B. M. Alauddin; Sharmin, Sabrina; Islam, Md Saiful
Introduction Hospital-acquired infections endanger millions of lives around the world, and nurses play a vital role in the prevention of these infections. Knowledge of infection prevention and control (IPC) best practices among nurses is a prerequisite to maintaining standard precautions for the safety of patients. Aim The study aims to assess knowledge, attitudes, and practices (KAP) towards IPC including associated factors among the nurses of a tertiary care hospital in Bangladesh. Methods We conducted this hospital-based cross-sectional study from October 2017 to June 2018 at Dhaka Medical College Hospital among 300 nurses working in all departments. We calculated three KAP scores for each participant reflecting their current state of knowledge and compliance towards IPC measures. Descriptive, bivariate and multivariable analyses were conducted to determine KAP scores among nurses and their associated factors. Results Average scores for knowledge, attitudes, and practices were 18.6, 5.4, and 15.5 (out of 26, 7, and 24), respectively. The study revealed that the majority (85.2%) of the nurses had a good to moderate level of knowledge, half (51%) of them showed positive attitudes, and only one fifth (17.1%) of the nurses displayed good practices in IPC. The respondents’ age, education, monthly income and years of experience were found to have statistical associations with having moderate to adequate level of KAP scores. Aged and experienced nurses were found more likely to have poor knowledge and unfavorable attitude toward IPC practices. Conclusion The majority of nurses had good IPC knowledge, but their practices did not reflect that knowledge. In particular, nurses needed to improve the proper IPC practice for better patient care and to protect themselves. Regular IPC training and practice monitoring can enhance the IPC practice among nurses.
Predicting Diabetes in Women through Machine Learning
(IEEE, 2024-04-18) Hasan, Rakib; Islam, Muksitul; Hosen, Md. Mamun; Abdullah-Al-Kafi, Md; Radhakrishnan, Niranchana
Diabetes, a predominant non-communicable illness, presents a significant worldwide open well-being concern with rising incidence rates and noteworthy mortality around the world. Timely determination is essential in moderating the weakening impacts of diabetes. This study utilized the Pima Indian Diabetes Dataset to form a predictive model for diabetes discovery through the application of machine learning strategies. In-depth investigation is done, carefully comparing Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT), Random Forests (RF), Ada Boosting (AB), and Gradient Boosting (GB). In terms of accuracy, precision, recall, and the F1 score, among other vital assessment measurements, they reliably and powerfully illustrate Random Forest’s extraordinary performance, and it is clearly the most excellent choice for diabetes forecasting, according to this study, giving medical experts an important instrument for exact, convenient, and timely identification of this common and serious sickness.
Predicting Diabetes in Women through Machine Learning
(2024-03-01) Hasan, Rakib; Islam, Muksitul; Hosen, Md. Mamun; Abdullah-Al-Kafi, Md; Radhakrishnan, Niranchana
Diabetes, a predominant non-communicable illness, presents a significant worldwide open well-being concern with rising incidence rates and noteworthy mortality around the world. Timely determination is essential in moderating the weakening impacts of diabetes. This study utilized the Pima Indian Diabetes Dataset to form a predictive model for diabetes discovery through the application of machine learning strategies. In-depth investigation is done, carefully comparing Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT), Random Forests (RF), Ada Boosting (AB), and Gradient Boosting (GB). In terms of accuracy, precision, recall, and the F1 score, among other vital assessment measurements, they reliably and powerfully illustrate Random Forest’s extraordinary performance, and it is clearly the most excellent choice for diabetes forecasting, according to this study, giving medical experts an important instrument for exact, convenient, and timely identification of this common and serious sickness.