Classifying Dental Care Providers Through Machine Learning with Features Ranking DOI
Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 755 - 755

Published: April 7, 2025

This study investigates the application of machine learning (ML) models for classifying dental providers into two categories—standard rendering and safety net clinic (SNC) providers—using a 2018 dataset 24,300 instances with 20 features. The dataset, characterized by high missing values (38.1%), includes service counts (preventive, treatment, exams), delivery systems (FFS, managed care), beneficiary demographics. Feature ranking methods such as information gain, Gini index, ANOVA were employed to identify critical predictors, revealing treatment-related metrics (TXMT_USER_CNT, TXMT_SVC_CNT) top-ranked Twelve ML models, including k-Nearest Neighbors (kNN), Decision Trees, Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Random Forest, Neural Networks, Boosting, evaluated using 10-fold cross-validation. Classification accuracy was tested across incremental feature subsets derived from rankings. Network achieved highest (94.1%) all features, followed Boosting (93.2%) Forest (93.0%). Models showed improved performance more features incorporated, SGD ensemble demonstrating robustness data. highlighted dominance treatment annotation codes in distinguishing provider types, while demographic variables (AGE_GROUP, CALENDAR_YEAR) had minimal impact. underscores importance selection enhancing model efficiency accuracy, particularly imbalanced healthcare datasets. These findings advocate integrating feature-ranking techniques advanced algorithms optimize classification, enabling targeted resource allocation underserved populations.

Language: Английский

Echoes in the Genome: Smoking’s Epigenetic Fingerprints and Bidirectional Neurobiological Pathways in Addiction and Disease DOI
Muhyeeddin Alqaraleh, Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 3

Published: Dec. 30, 2024

Smoking remains a global health crisis, contributing to addiction and diverse diseases through complex biological mechanisms. This study explores the hypothesis that smoking induces epigenetic modifications alters bidirectional neurobiological pathways, perpetuating disease progression. Leveraging dataset of 55,692 individuals with 27 metrics, we analyze associations between status physiological markers (e.g., lipid profiles, blood pressure, liver enzymes) infer potential mediators. Preliminary data reveal significant correlations elevated triglycerides, LDL cholesterol, function markers, suggesting systemic inflammation oxidative stress as plausible intermediaries. We propose methodology integrating bioinformatics systems biology map smoking-associated phenotypic changes loci DNA methylation) neural circuits dopaminergic pathways). work aims bridge clinical observations molecular mechanisms, offering insights into personalized interventions targeting smoking’s "fingerprints" their consequences.

Language: Английский

Citations

4
From Puffs to Predictions: Leveraging AI, Wearables, and Biomolecular Signatures to Decode Smoking’s Multidimensional Impact on Cardiovascular Health DOI
Muhyeeddin Alqaraleh, Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: 3

Published: Dec. 30, 2024

Tobacco smoking keeps to exert a profound effect on cardiovascular health, contributing situations including arterial stiffness, hypertension, and microcirculatory disorder. Traditional studies strategies, often siloed into remoted domains like biomarker analysis or behavioral surveys, fail seize the dynamic interplay between behaviors biological disruptions. This take look at integrates AI-driven analytics, wearable sensor networks, deep biomolecular profiling map smoking’s multidimensional effects. By combining actual-time physiological statistics (e.g., PPG, HRV) with epigenetic proteomic markers, research objectives are expecting individual risks enable preemptive interventions. Results reveal efficacy of ensemble models Random Forest (AUC = zero.889) in taking pictures complex interactions among variables consisting γ-GTP, waist circumference, blood stress. The paintings highlight capability AI wearables convert reactive healthcare personalized, preventive strategies.

Language: Английский

Citations

4
Revolutionizing Blood Banks: AI-Driven Fingerprint-Blood Group Correlation for Enhanced Safety DOI
Malik A. Altayar, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 894 - 894

Published: April 7, 2025

Identification of a person is central in forensic science, security, and healthcare. Methods such as iris scanning genomic profiling are more accurate but expensive, time-consuming, difficult to implement. This study focuses on the relationship between fingerprint patterns ABO blood group biometric identification tool. A total 200 subjects were included study, types (loops, whorls, arches) groups compared. Associations evaluated with statistical tests, including chi-square Pearson correlation.The found that loops most common pattern O+ was prevalent. Discussion: Even though there some associative pattern, no statistically significant difference different groups. Overall, results indicate data do not significantly improve personal when used conjunction fingerprinting.Although shows weak correlation, it may emphasize efforts multi-modal based systems enhancing current systems. Future studies focus larger diverse samples, possibly machine learning additional biometrics methods. addresses an element ever-changing nature fields science identification, highlighting importance resilient analytical methods for identification.

Language: Английский

Citations

0
Predicting Blood Type: Assessing Model Performance with ROC Analysis DOI
Malik A. Altayar, Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 895 - 895

Published: April 9, 2025

Introduction: Personal identification is a critical aspect of forensic sciences, security, and healthcare. While conventional biometrics systems such as DNA profiling iris scanning offer high accuracy, they are time-consuming costly. Objectives: This study investigates the relationship between fingerprint patterns ABO blood group classification to explore potential correlations these two traits.Methods: The analyzed 200 individuals, categorizing their fingerprints into three types: loops, whorls, arches. Blood was also recorded. Statistical analysis, including chi-square Pearson correlation tests, used assess associations groups.Results: Loops were most common pattern, while O+ prevalent among participants. analysis revealed no significant groups (p > 0.05), suggesting that traits independent.Conclusions: Although showed limited groups, it highlights importance future research using larger more diverse populations, incorporating machine learning approaches, integrating multiple biometric signals. contributes science by emphasizing need for rigorous protocols comprehensive investigations in personal identification.

Language: Английский

Citations

0
Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency DOI Creative Commons
Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 756 - 756

Published: March 19, 2025

Vesicoureteral reflux (VUR) is traditionally assessed using subjective grading systems, leading to variability in diagnosis. This study explores the potential of machine learning enhance diagnostic accuracy by analysing voiding cystourethrogram (VCUG) images. The objective develop predictive models that provide an and consistent approach VUR classification. A total 113 VCUG images were reviewed, with experts them based on severity. Nine distinct image features selected build six models, which evaluated 'leave-one-out' cross-validation. analysis identified renal calyces’ deformation patterns as key indicators high-grade VUR. models—Logistic Regression, Tree, Gradient Boosting, Neural Network, Stochastic Descent—achieved precise classifications no false positives or negatives. High sensitivity subtle characteristic different grades was confirmed substantial Area Under Curve (AUC) values. demonstrates can address limitations assessments, offering a more reliable standardized system. findings highlight significance predictor severe cases. Future research should focus refining methodologies, exploring additional features, expanding dataset model clinical applicability.

Language: Английский

Citations

0
Classifying Dental Care Providers Through Machine Learning with Features Ranking DOI
Mohammad Subhi Al-Batah, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 755 - 755

Published: April 7, 2025

This study investigates the application of machine learning (ML) models for classifying dental providers into two categories—standard rendering and safety net clinic (SNC) providers—using a 2018 dataset 24,300 instances with 20 features. The dataset, characterized by high missing values (38.1%), includes service counts (preventive, treatment, exams), delivery systems (FFS, managed care), beneficiary demographics. Feature ranking methods such as information gain, Gini index, ANOVA were employed to identify critical predictors, revealing treatment-related metrics (TXMT_USER_CNT, TXMT_SVC_CNT) top-ranked Twelve ML models, including k-Nearest Neighbors (kNN), Decision Trees, Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Random Forest, Neural Networks, Boosting, evaluated using 10-fold cross-validation. Classification accuracy was tested across incremental feature subsets derived from rankings. Network achieved highest (94.1%) all features, followed Boosting (93.2%) Forest (93.0%). Models showed improved performance more features incorporated, SGD ensemble demonstrating robustness data. highlighted dominance treatment annotation codes in distinguishing provider types, while demographic variables (AGE_GROUP, CALENDAR_YEAR) had minimal impact. underscores importance selection enhancing model efficiency accuracy, particularly imbalanced healthcare datasets. These findings advocate integrating feature-ranking techniques advanced algorithms optimize classification, enabling targeted resource allocation underserved populations.

Language: Английский

Citations

0