Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11413/6817
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Browsing Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering by Publisher "Elsevier Ireland Ltd, Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland"
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Publication Automatic energy expenditure measurement for health science(Elsevier Ireland Ltd, Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland, 2018-04) Çatal, Çağatay; AKBULUT, AKHAN; 108363; 116056Background and objective: It is crucial to predict the human energy expenditure in any sports activity and health science application accurately to investigate the impact of the activity. However, measurement of the real energy expenditure is not a trivial task and involves complex steps. The objective of this work is to improve the performance of existing estimation models of energy expenditure by using machine learning algorithms and several data from different sensors and provide this estimation service in a cloud-based platform. Methods: In this study, we used input data such as breathe rate, and hearth rate from three sensors. Inputs are received from a web form and sent to the web service which applies a regression model on Azure cloud platform. During the experiments, we assessed several machine learning models based on regression methods. Results: Our experimental results showed that our novel model which applies Boosted Decision Tree Regression in conjunction with the median aggregation technique provides the best result among other five regression algorithms. Conclusions: This cloud-based energy expenditure system which uses a web service showed that cloud computing technology is a great opportunity to develop estimation systems and the new model which applies Boosted Decision Tree Regression with the median aggregation provides remarkable results. (C) 2018 Elsevier B.V. All rights reserved.Publication Fetal health status prediction based on maternal clinical history using machine learning techniques(Elsevier Ireland Ltd, Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland, 2018) Ertuğrul, Egemen; AKBULUT, AKHAN; 116056Background and Objective: Congenital anomalies are seen at 1-3% of the population, probabilities of which are tried to be found out primarily through double, triple and quad tests during pregnancy. Also, ultra-sonographical evaluations of fetuses enhance detecting and defining these abnormalities. About 60-70% of the anomalies can be diagnosed via ultra-sonography, while the remaining 30-40% can be diagnosed after childbirth. Medical diagnosis and prediction is a topic that is closely related with e-Health and machine learning. e-Health applications are critically important especially for the patients unable to see a doctor or any health professional. Our objective is to help clinicians and families to better predict fetal congenital anomalies besides the traditional pregnancy tests using machine learning techniques and e-Health applications. Methods: In this work, we developed a prediction system with assistive e-Health applications which both the pregnant women and practitioners can make use of. A performance comparison (considering Accuracy, Fl-Score, AUC measures) was made between 9 binary classification models (Averaged Perceptron, Boosted Decision Tree, Bayes Point Machine, Decision Forest, Decision Jungle, Locally-Deep Support Vector Machine, Logistic Regression, Neural Network, Support Vector Machine) which were trained with the clinical dataset of 96 pregnant women and used to process data to predict fetal anomaly status based on the maternal and clinical data. The dataset was obtained through maternal questionnaire and detailed evaluations of 3 clinicians from RadyoEmar radiodiagnostics center in Istanbul, Turkey. Our e-Health applications are used to get pregnant women's health status and clinical history parameters as inputs, recommend them physical activities to perform during pregnancy, and inform the practitioners and finally the patients about possible risks of fetal anomalies as the output. Results: In this paper, the highest accuracy of prediction was displayed as 89.5% during the development tests with Decision Forest model. In real life testing with 16 users, the performance was 87.5%. This estimate is sufficient to give an idea of fetal health before the patient visits the physician. Conclusions: The proposed work aims to provide assistive services to pregnant women and clinicians via an online system consisting of a mobile side for the patients, a web application side for their clinicians and a prediction system. In addition, we showed the impact of certain clinical data parameters of pregnant on the fetal health status, statistically correlated the parameters with the existence of fetal anomalies and showed guidelines for future researches. (C) 2018 Elsevier B.V. All rights reserved.