A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment

2021 ◽  
Vol 107 ◽  
pp. 107391
Author(s):  
Pantelis Z. Lappas ◽  
Athanasios N. Yannacopoulos
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Credit Risk ◽  
Expert Knowledge ◽  
Learning Approach ◽  
Credit Risk Assessment ◽  
Machine Learning Approach ◽  
Selection For

Automated feature selection for a machine learning approach toward modeling a mosquito distribution

2017 ◽  
Vol 352 ◽  
pp. 108-112 ◽  
Author(s):  
Ralf Wieland ◽  
Antje Kerkow ◽  
Linus Früh ◽  
Helge Kampen ◽  
Doreen Walther
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Selection For

scholarly journals Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach

2016 ◽  
Vol 11 (4) ◽  
pp. 791-799 ◽  
Author(s):  
Rina Kagawa ◽  
Yoshimasa Kawazoe ◽  
Yusuke Ida ◽  
Emiko Shinohara ◽  
Katsuya Tanaka ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Expert Knowledge ◽  
The Other ◽  
Learning Approach ◽  
Research Subjects ◽  
Machine Learning Approach

Background: Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotyping has been increasing. Some existing phenotyping algorithms are not sufficiently accurate for screening or identifying clinical research subjects. Objective: We propose a practical phenotyping framework using both expert knowledge and a machine learning approach to develop 2 phenotyping algorithms: one is for screening; the other is for identifying research subjects. Methods: We employ expert knowledge as rules to exclude obvious control patients and machine learning to increase accuracy for complicated patients. We developed phenotyping algorithms on the basis of our framework and performed binary classification to determine whether a patient has T2DM. To facilitate development of practical phenotyping algorithms, this study introduces new evaluation metrics: area under the precision-sensitivity curve (AUPS) with a high sensitivity and AUPS with a high positive predictive value. Results: The proposed phenotyping algorithms based on our framework show higher performance than baseline algorithms. Our proposed framework can be used to develop 2 types of phenotyping algorithms depending on the tuning approach: one for screening, the other for identifying research subjects. Conclusions: We develop a novel phenotyping framework that can be easily implemented on the basis of proper evaluation metrics, which are in accordance with users’ objectives. The phenotyping algorithms based on our framework are useful for extraction of T2DM patients in retrospective studies.

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