Interpretability in healthcare: A comparative study of local machine learning interpretability techniques

Mathematics

• Interpretability 100%
• Healthcare 92%
• Comparative Study 84%
• Machine Learning 82%
• High Performance 49%
• Prediction 40%
• Experimental Evaluation 36%
• Execution Time 33%
• Separability 27%
• Metric 24%
• Model 24%
• Comprehensive Evaluation 22%
• Random Forest 18%
• Prediction Model 16%
• Decision tree 16%
• Time-average 15%
• Lowest 12%
• Linear Regression Model 12%
• Neural Networks 11%
• Similarity 10%
• Experiment 9%
• Output 9%
• Cover 9%
• Context 7%
• Performance 7%

Engineering & Materials Science

• Machine learning 55%
• Anchors 19%
• Random forests 9%
• Data privacy 9%
• Linear regression 9%
• Decision trees 9%
• Neural networks 5%
• Experiments 3%