Automated extraction of decision rules for leptin dynamics—A rough sets approach

Noninvasive evaluation of mental stress using by a refined rough set technique based on biomedical signals

OBJECTIVE

Evaluating and treating of stress can substantially benefits to people with health problems. Currently, mental stress evaluated using medical questionnaires. However, the accuracy of this evaluation method is questionable because of variations caused by factors such as cultural differences and individual subjectivity. Measuring of biomedical signals is an effective method for estimating mental stress that enables this problem to be overcome. However, the relationship between the levels of mental stress and biomedical signals remain poorly understood.

METHODS AND MATERIALS

A refined rough set algorithm is proposed to determine the relationship between mental stress and biomedical signals, this algorithm combines rough set theory with a hybrid Taguchi-genetic algorithm, called RS-HTGA. Two parameters were used for evaluating the performance of the proposed RS-HTGA method. A dataset obtained from a practice clinic comprising 362 cases (196 male, 166 female) was adopted to evaluate the performance of the proposed approach.

RESULTS

The empirical results indicate that the proposed method can achieve acceptable accuracy in medical practice. Furthermore, the proposed method was successfully used to identify the relationship between mental stress levels and bio-medical signals. In addition, the comparison between the RS-HTGA and a support vector machine (SVM) method indicated that both methods yield good results. The total averages for sensitivity, specificity, and precision were greater than 96%, the results indicated that both algorithms produced highly accurate results, but a substantial difference in discrimination existed among people with Phase 0 stress. The SVM algorithm shows 89% and the RS-HTGA shows 96%. Therefore, the RS-HTGA is superior to the SVM algorithm. The kappa test results for both algorithms were greater than 0.936, indicating high accuracy and consistency. The area under receiver operating characteristic curve for both the RS-HTGA and a SVM method were greater than 0.77, indicating a good discrimination capability.

CONCLUSIONS

In this study, crucial attributes in stress evaluation were successfully recognized using biomedical signals, thereby enabling the conservation of medical resources and elucidating the mapping relationship between levels of mental stress and candidate attributes. In addition, we developed a prototype system for mental stress evaluation that can be used to provide benefits in medical practice.

The synthesis of the rough set model for the better applicability of sagittal abdominal diameter in identifying high risk patients

This paper aims to investigate possible application of the rough set approach to table-organized data in the medical domain, which reveals some relationships among sagittal abdominal diameter, anthropometric parameters and cardiovascular risk factors. When applied to table-organized data, the methodology based on the rough set theory is capable of producing decision rules in the form of If-Then rules. Such rules are suitable for inspection, examination and further analysis. By examination of the selected 30 decision rules, sagittal abdominal diameter could point out a group of obese and preobese patients with high content of visceral fat with different combination and composition of cardiovascular risk factors. These results suggest that sagittal abdominal diameter could be a clinically useful marker for identification of risk factors, combination and structure of total cardiovascular risk by applying different rules in obese and preobese persons.