Cervical cancer risk assessment using machine learning integrated fuzzy MCDM methodology

Keyphrases

• Machine Learning 100%
• Decision-making Method 100%
• Risk Level 100%
• FMCDM 100%
• Cancer Risk Assessment 100%
• Cervical Cancer Incidence 100%
• Cervical Cancer 75%
• Proposed Methodology 50%
• Feature Selection 25%
• Feature Selection Methods 25%
• Machine Learning Techniques 25%
• Context-based 25%
• Detection Method 25%
• Potential Treatments 25%
• Pythagorean Fuzzy 25%
• Early Detection 25%
• Correlation Method 25%
• Effective Criteria 25%
• Average Solution 25%
• UCI Dataset 25%
• Order Preference 25%
• Early Identification 25%
• Cervical Cancer Screening 25%
• Behavioral Criteria 25%
• Behavioral Risk Factors 25%
• Multi-criteria Decision-making Problem 25%
• Current Rate 25%
• Risk Data 25%
• Cancer Screening Participation 25%

Computer Science

• Feature Selection 100%
• Machine Learning 100%
• Learning System 100%
• Feature Extraction 100%
• Early Detection 50%
• Preference Order 50%
• Correlation Method 50%
• Evaluation Based On Distance From Average Solution 50%
• Machine Learning Technique 50%

Engineering

• Multi-Criteria Decision-Making 100%
• Risk Level 100%
• Learning System 100%
• Feature Extraction 50%
• Similarities 25%
• Early Stage 25%
• Selection Algorithm 25%
• Making Problem 25%
• Ideal Solution 25%
• Machine Learning Technique 25%
• Early Detection 25%
• Preference Order 25%
• Correlation Method 25%