Feature Selection in Associative Classification-A Review and Comparative Analysis

This article uses machine learning to quantify vesicoureteral reflux (VUR). VCUGs in pediatric urology are used to diagnose VUR. The goal is to increase diagnostic precision. Various machine learning models categorize VUR grades (Grade 1 to Grade 5) and are evaluated using performance metrics and confusion matrices. Study datasets come from internet repositories with repository names and accession numbers. Machine learning models performed well across several measures. KNN, Random Forest, AdaBoost, and CN2 Rule Induction consistently scored 100% in AUC, CA, F1-score, precision, recall, MCC, and specificity. These models classified grades well individually and collectively. In contrast, the Constant model performed poorly across all criteria, suggesting its inability to categorize VUR grades reliably. With the most excellent average performance ratings, the CN2 Rule Induction model excelled at grade categorization. Confusion matrices demonstrate that machine learning models predict VUR grades. The large diagonal numbers of the matrices show that the models are regularly predicted effectively. However, the Constant model's constant Grade 5 forecast reduced its differentiation. This study shows that most machine learning methods automate VUR measurement. The findings aid objective pediatric urology grading and radiographic evaluation. The CN2 Rule Induction model accurately classifies VUR grades. Machine learning-based diagnostic techniques may increase diagnostic precision, clinical decision-making, and patient outcomes.

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Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging

Al-Batah,

Salem Alzboon,

Alqaraleh

2025

Data and Metadata

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Brain cancer remains one of the most challenging medical conditions due to its intricate nature and the critical functions of the brain. Effective diagnostic and treatment strategies are essential, particularly given the high stakes involved in early detection. Magnetic Resonance (MR) imaging has emerged as a crucial modality for the identification and monitoring of brain tumors, offering detailed insights into tumor morphology and behavior. Recent advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the analysis of medical imaging, significantly enhancing diagnostic precision and efficiency. This study classifies three primary brain tumor types—glioma, meningioma, and general brain tumors—utilizing a comprehensive dataset comprising 15,000 MR images obtained from Kaggle. We evaluated the performance of six distinct machine learning models: K-Nearest Neighbors (KNN), Neural Networks, Logistic Regression, Support Vector Machine (SVM), Decision Trees, and Random Forests. Each model's effectiveness was assessed through multiple metrics, including classification accuracy (CA), Area Under the Curve (AUC), F1 score, precision, and recall. Our findings reveal that KNN and Neural Networks achieved remarkable classification accuracies of 98.5% and 98.4%, respectively, significantly surpassing the performance of other evaluated models. These results underscore the promise of ML algorithms, particularly KNN and Neural Networks, in improving the diagnostic process for brain cancer through MR imaging. Future research will focus on validating these models with real-world clinical data, aiming to refine and enhance diagnostic methodologies, thus contributing to the development of more accurate, efficient, and accessible tools for brain cancer diagnosis and management.

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Feature Selection in Associative Classification-A Review and Comparative Analysis

Cited by 3 publications

References 22 publications

Credit Card Fraud Detection Algorithm Based on Improved Particle Swarm Optimization

Credit Card Fraud Detection Algorithm Based on Improved Particle Swarm Optimization

Automated Quantification of Vesicoureteral Reflux using Machine Learning with Advancing Diagnostic Precision

Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging

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