C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Tabu Search
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Computers and Industrial Engineering
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Tabu search for attribute reduction in rough set theory
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Soft Computing: Integrating Evolutionary, Neural, and Fuzzy Systems
Soft Computing: Integrating Evolutionary, Neural, and Fuzzy Systems
Inelligent ensemble system aids osteoporosis early detection
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
International Journal of Healthcare Information Systems and Informatics
Classifying Consumer Comparison Opinions to Uncover Product Strengths and Weaknesses
International Journal of Intelligent Information Technologies
Sociomateriality Implications of Multi-Agent Supported Collaborative Work Systems
International Journal of Intelligent Information Technologies
Low Dimensional Data Privacy Preservation Using Multi Layer Artificial Neural Network
International Journal of Intelligent Information Technologies
A Semi-Supervised Learning Approach to Integrated Salient Risk Features for Bone Diseases
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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The healthcare environment is generally perceived as being information rich yet knowledge poor. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not "mined" to discover hidden information. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. The information technology may provide alternative approaches to Osteoporosis disease diagnosis. This study examines the potential use of classification techniques on a massive volume of healthcare data, particularly in prediction of patients that may have Osteoporosis Disease OD through its risk factors. The paper proposes to develop a dynamic rough sets solution approach in order to generate dynamic reduced subsets of features associated with a classification model using Random Forest RF decision tree to identify the osteoporosis cases. There has been no research in using the afore-mentioned algorithm for Osteoporosis patients' prediction. The reduction of the attributes consists of enumerating dynamically the optimal subsets of the most relevant attributes by reducing the degree of complexity. An intelligent decision support system is developed for this purpose. The study population consisted of 2845 adults. The performance of the proposed model is analyzed and evaluated based on a set of benchmark techniques applied in this classification problem.