Using evolutionary rough sets on stress prediction model by biomedical signal

Liu, Tung-Kuan; Chen, Yeh-Peng; Zheng, Zi-Jing; Wang, Chao-Chih; Hou, Zone-Yuan; Chen, Chiu-Hung; Chou, Jyh–Horng

doi:10.1109/icmlc.2011.6016702

Cited by 2 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Nomura et al ( 2010 ), the authors use rough sets instead of conventional linear correlation analysis for mining the relationship between a subjective stress scale and salivary cortisol stress biomarker. In Liu et al ( 2011 ), a hybrid rough set and Taguchi-genetic algorithm (RS-HTGA) was proposed to determine the relationship between mental stress and biomedical signals. The efficacy of their model was tested on a clinical dataset comprising 362 cases (196 male, 166 female).…”

Section: Introductionmentioning

confidence: 99%

Rough-set based learning: Assessing patterns and predictability of anxiety, depression, and sleep scores associated with the use of cannabinoid-based medicine during COVID-19

Ramanna

Ashrafi²,

Loster³

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

Recently, research is emerging highlighting the potential of cannabinoids' beneficial effects related to anxiety, mood, and sleep disorders as well as pointing to an increased use of cannabinoid-based medicines since COVID-19 was declared a pandemic. The objective of this research is 3 fold: i) to evaluate the relationship of the clinical delivery of cannabinoid-based medicine for anxiety, depression and sleep scores by utilizing machine learning specifically rough set methods; ii) to discover patterns based on patient features such as specific cannabinoid recommendations, diagnosis information, decreasing/increasing levels of clinical assessment tools (CAT) scores over a period of time; and iii) to predict whether new patients could potentially experience either an increase or decrease in CAT scores. The dataset for this study was derived from patient visits to Ekosi Health Centres, Canada over a 2 year period including the COVID timeline. Extensive pre-processing and feature engineering was performed. A class feature indicative of their progress or lack thereof due to the treatment received was introduced. Six Rough/Fuzzy-Rough classifiers as well as Random Forest and RIPPER classifiers were trained on the patient dataset using a 10-fold stratified CV method. The highest overall accuracy, sensitivity and specificity measures of over 99% was obtained using the rule-based rough-set learning model. In this study, we have identified rough-set based machine learning model with high accuracy that could be utilized for future studies regarding cannabinoids and precision medicine.

show abstract

Section: Introductionmentioning

confidence: 99%

Rough-set based learning: Assessing patterns and predictability of anxiety, depression, and sleep scores associated with the use of cannabinoid-based medicine during COVID-19

Ramanna

Ashrafi²,

Loster³

et al. 2023

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…In [29], the authors use rough sets instead of conventional linear correlation analysis for mining the relationship between a subjective stress scale and salivary cortisol stress biomarker. In [30], a hybrid rough set and Taguchi-genetic algorithm (RS-HTGA) was proposed to determine the relationship between mental stress and biomedical signals. The efficacy of their method was tested on a clinical dataset comprising 362 cases (196 male, 166 female).…”

Section: Introductionmentioning

confidence: 99%

Rough-set based learning methods: A case study to assess the relationship between the clinical delivery of cannabinoid medicine for anxiety, depression, sleep, patterns and predictability

Ashrafi¹

View full text Add to dashboard Cite

COVID-19 is an unprecedented health crisis causing a great deal of stress and mental health challenges in populations in Canada. Recently, research is emerging highlighting the potential of cannabinoids’ beneficial effects related to anxiety, mood, and sleep disorders as well as pointing to an increased use of medicinal cannabis since COVID-19 was declared a pandemic. Furthermore, evidence points to a correlation between mental health and sleep patterns. The objective of this research is threefold: i) to assess the relationship of the clinical delivery of cannabinoid medicine, by utilizing machine learning, to anxiety, depression and sleep scores; ii) to discover patterns based on patient features such as specific cannabis recommendations, diagnosis information, decreasing/increasing levels of clinical assessment tools (GAD7, PHQ9 and PSQI) scores over a period of time (including during the COVID timeline); and iii) to predict whether new patients could potentially experience either an increase or decrease in clinical assessment tool scores. The dataset for this thesis was derived from patient visits to Ekosi Health Centres in Manitoba, Canada and Ontario, Canada from January, 2019 to April, 2021. Extensive pre-processing and feature engineering was performed. To determine the outcome of a patients treatment, a class feature (Worse, Better, or No Change) indicative of their progress or lack thereof due to the treatment received was introduced. Three well-known supervised machine learning models (tree-based, rule-based and nearest neighbour) were trained on the patient dataset. In addition, seven rough and rough-fuzzy hybrid methods were also trained on the same dataset. All experiments were conducted using a 10-fold CV method. Sensitivity and specificity measures were higher in all classes with rough and rough-fuzzy hybrid methods. The highest accuracy of 99.15% was obtained using the rule-based rough-set learning method.

show abstract

Using evolutionary rough sets on stress prediction model by biomedical signal

Cited by 2 publications

References 14 publications

Rough-set based learning: Assessing patterns and predictability of anxiety, depression, and sleep scores associated with the use of cannabinoid-based medicine during COVID-19

Rough-set based learning: Assessing patterns and predictability of anxiety, depression, and sleep scores associated with the use of cannabinoid-based medicine during COVID-19

Rough-set based learning methods: A case study to assess the relationship between the clinical delivery of cannabinoid medicine for anxiety, depression, sleep, patterns and predictability

Contact Info

Product

Resources

About