Browsing by Author "Ucar, Kubra"

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    Citation Count: 1
    Determination gender-based hybrid artificial intelligence of body muscle percentage by photoplethysmography signal
    (Elsevier Ireland Ltd, 2022) Ucar, Muhammed Kuersad; Ucar, Kubra; Ucar, Zeliha; Bozkurt, Mehmet Recep
    Background and objective: Muscle mass is one of the critical components that ensure muscle function. Loss of muscle mass at every stage of life can cause many adverse effects. Sarcopenia, which can occur in different age groups and is characterized by a decrease in muscle mass, is a critical syndrome that affects the quality of life of individuals. Aging, a universal process, can also cause loss of muscle mass. It is essential to monitor and measure muscle mass, which should be sufficient to maintain optimal health. Having various disadvantages with the ordinary methods used to estimate muscle mass increases the need for the new high technology methods. This study aims to develop a low-cost and trustworthy Body Muscle Percentage calculation model based on artificial intelligence algorithms and biomedical signals.Methods: For the study, 327 photoplethysmography signals of the subject were used. First, the photo-plethysmography signals were filtered, and sub-frequency bands were obtained. A quantity of 125 time -domain features, 25 from each signal, have been extracted. Additionally, it has reached 130 features in demographic features added to the model. To enhance the performance, the spearman feature selection algorithm was used. Decision trees, Support Vector Machines, Ensemble Decision Trees, and Hybrid ma-chine learning algorithms (the combination of three methods) were used as machine learning algorithms.Results: The recommended Body Muscle Percentage estimation model have the perfomance values for all individuals R = 0 . 95 , for males R = 0 . 90 and for females R = 0 . 90 in this study.Conclusion: Regarding the study results, it is thought that photoplethysmography-based models can be used to predict body muscle percentage.(c) 2022 Elsevier B.V. All rights reserved.
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    Citation Count: 6
    Determination of body fat percentage by electrocardiography signal with gender based artificial intelligence
    (Elsevier Sci Ltd, 2021) Ucar, Muhammed Kursad; Ucar, Zeliha; Ucar, Kubra; Akman, Mehmet; Bozkurt, Mehmet Recep
    Background and purpose: Body fat percentage (BFP) is a frequently used parameter in the assessment of body composition. The body is made up of fat, muscle and lean body tissues. Excess fat tissue in the body causes obesity. Obesity is a treatable disease that decreases the quality of life. Obesity can trigger ailments such as psychological disorders, cardiovascular diseases and respiratory and digestive problems. Dual energy X-ray absorptiometry gold standard method is laborious, costly and time consuming. For this reason, more practical methods are needed. The aim of this study is to develop BFP prediction models with gender-based electrocardiography (ECG) signal and machine learning methods. Methods: In the study, 25 features were extracted from seven different QRS bands and filtered and unfiltered ECG signals. In addition, age, height and weight were used as features. Spearman feature selection algorithm was used to increase the performance. Results: The BFP prediction models developed have performance values of R = 0.94 for men and R = 0.93 for women and R = 0.91 for all individuals. Feature selection algorithm helped increase performance. Conclusion