Energy must be taken regularly for the body to continue its life healthily. Age, gender, physical activity, height, and weight determine the energy required to sustain life. There are various methods used to measure energy expenditure. These methods; methods such as direct calorimetry, gold standard indirect calorimetry, respiratory coefficient, and double effect, can be counted. These methods can be counted as; direct calorimetry, gold standard indirect calorimetry, respiratory coefficient, and double effect. Therefore, newmethods are needed. This study proposes anthropometric and demographic information on the individual and artificial intelligence-based energy needs estimation. Artificial intelligence models were created by selecting 14 characteristics of individuals with the help of selection. Linear, Gaussian Process Regression (GPR), Neural Network (NN), Support Vector Machine (SVM), and ensemble methods were used as artificial intelligence models and developed gender-based models for increased performance. The best model performance was determined in the models developed for all individuals as RMSE = 1.6, R2 = 1 MSE = 2.58, and MAE = 0.74 with a GPR-based 50

Objective: Calculation of body fat percentage (BFP) is a frequently encountered problem in the literature. BFP is one of the most significant parameters which should be processed in body weight control programs. Anthropometric measurements and statistical methods are being used generally in the literature for BFP estimation. Artificial intelligence and gender-based models with a photoplethysmography signal (PPG) were proposed for BFP estimation in this study. Material and Methods: In the study, the PPG signal is divided into lower frequency bands, and 25 features are taken out from each frequency band. Artificial intelligence algorithms were created by reducing the extracted features with the help of a feature selection algorithm. Results: According to the results obtained, models with performance values of RMSE = 0.35, R =1 for men, RMSE = 0.87, R =1 for women were created. Conclusions: In the best performing models, the PPG signal's high-frequency components are used for men, whereas the low-frequency band of the PPG signal is used for women. As a result, the proposed model in this study is considered to be used for BFP measurement.

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 photoplethysmography 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 machine 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.

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:: According to the results, it is thought that ECG based BFP prediction models can be used in practice.

Objective: As one of the sustainable diets, the Mediterranean Diet (MD) is one of the healthiest diets in the world. The aim of this study is to determine the effect of MD, which is one of the dietary models supporting healthy nutrition, on biochemical parameters in adult individuals. MD supports healthy life and is important in preventing chronic diseases. Methods: study was conducted with a total of 122 individuals between the ages of 18-64 who applied to Sakarya University Healthy Nutrition / Obesity Counseling Unit between September 2019 and February 2020. The data were collected by face-to-face interview technique. Collected data are; demographic information, Mediterranean diet compliance scale survey, and biochemical parameters. Results: According to the results obtained, the erythrocyte and fasting insulin in individuals were found to be significant with the Mediterranean diet compliance (p <0.05). Homeostatic Model Assessment-Insulin Resistance (HOMA-IR) and fasting insulin were found to be significant with Mediterranean diet compliance in the analyzes performed for women (p <0.05). Conclusion: As a result; The findings obtained from this study showed that the Mediterranean diet reduced the risk of macro and micro complications caused by Diabetes Mellitus.

Bazal Metabolizma Hızı (BMH) günlük harcanan ve alınması gereken enerji hakkında bilinmesi gereken en önemli unsurlardan biridir. Literatürde genellikle kalorimetreler ve birtakım denklemler tarafından tespit edilmektedir. Bu çalışmada BMH tahmini için elektrokardiyografi (ECG) sinyalleri ile yapay zekâ tabanlı bir model oluşturulmuştur. Öncelikle bireylerden toplanan ECG sinyalleri gürültülerden temizlenip filtrelenmiştir. Daha sonra özellik çıkartılıp özellik seçme algoritmaları yardımıyla azaltılmıştır. Elde kalan özelliklerle yapay zekâ algoritmaları sayesinde BMH tahmininde bulunulmuştur. Erkekler için R = 0.91, kadınlar için R = 0.99 değerlerine sahip modeller oluşturulmuştur. Performans değerlendirme kriterleri de göz önüne alınarak en iyi model kadınlar için de erkekler için de Linear Regression modeli seçilmiştir. Tüm bu sonuçlara bakıldığında günlük hayatta BMH tahmini için önerilen modelin kullanılabileceği belirlenmiştir.

Obezite tedavisinde hedef yağ kütlesinin azaltılması amaçlanır. Bu yüzden, vücut yağ yüzdesinin hesap-lanması önemlidir. Bu çalışma, vücut yağ yüzdesinin hesabı için literatürdeki makalelerin sistematik bir derleme şeklinde sunulmasını amaçlar. Makale taraması için Sakarya Üniversitesi "Akademik Arama – EDS" platformu kullanılmıştır. Arama için "Body Fat Percentage Calculation", "Body Fat Percentage Estimation", "Body Fat Per-centage Equations" ve "Body Fat Percentage Prediction" anahtar kelimeleri kullanılmıştır. Anahtar kelimeler li-teratürde son yıllarda vücut yağ yüzdesi hesaplaması üzerine yapılan çalışmalarda kullanılan anahtar kelimelerdir. Diğer arama kriterleri şunlardır. Dil: İngilizce, Yayın Tarihi: 2000-2019. Yayınlar sadece hakemli dergilerden elde edilmiştir. Toplam 234 makale elde edilmiştir. Dahil edilme kriterlerine göre 234 makaleden 31 makale sis-tematik derleme kapsamında değerlendirilmiştir. Türkiye içerisinde yapılmış çalışmalar da araştırılmış olup Türkçe veya İngilizce herhangi bir çalışmaya rastlanılmamıştır. Elde edilen bulgulara göre, vücut yağ yüzdesi hesaplaması için sıklıkla antropometrik ölçümler kullanılmıştır. Eşitlik çıkarmak için istatistiksel temelli klasik yöntemler tercih edilmiştir. Eşitlik korelasyon değerleri 0.42< R<0.99 arasında değişkenlik göstermektedir. Eşitliklerin performansını belirleyen en önemli faktörler yaş, cinsiyet, etnik köken ve antropometrik ölçümler olduğu tespit edilmiştir. Sonuç olarak vücut yağ yüzdesi hesabı için yaş etnik yapı ve diğer parametreler göz önüne alınarak erkek ve kadınlar için ayrı eşitliklerin geliştirilebilir ve kullanılabilir.

Before obesity treatment, body fat percentage (BFP) should be determined. BFP cannot be measured by weighing. The devices developed to produce solutions to this problem are called "Body Analysis Devices". These devices are very costly. Therefore, more practical and cost-effective solutions are needed. This study aims to determine BFP using hybrid machine learning methods with high accuracy rate and minimum parameter. This study uses real data sets, which are 13 anthropometric measurements of individuals. Different feature groups were created with feature selection algorithm. In the next step, 4 different hybrid models were created by using MLFFNN, SVMs, and DT regression models. According to the results, BFP of individuals can be estimated with a correlation value of R = 0.79 with one anthropometric measurement. The results show that the developed system can be used to estimate BFP in practice. Besides, the system can calculate BFP with just one anthropometric measurement without device requirement. (C) 2020 Elsevier Ltd. All rights reserved.