• International Journal of Computer Science & Network Security
• /
• 제23권8호
• /
• pp.17-25
• /
• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Journal of Information Technology Applications and Management
• /
• 제27권3호
• /
• pp.19-36
• /
• 2020

The Internet of things (IoT) is a system of interrelated computed devices, digital machines and any physical objects which are provided with unique identifiers and the potential to transmit data to people or machine (M2M) without requiring human interaction. IoT devices can be used to monitor and control the electrical and electronic systems used in different fields like smart home, smart city, smart healthcare and etc. In this study we introduce four imaginary IoT devices as a learning support assistants according to students' dominant learning styles measured by Honey and Mumford Learning Styles: Activists, Reflectors, Theorists and Pragmatists. This research emphasizes the association between students' strong learning styles and a preference to appropriate IoT devices with specific characteristics. Moreover, different levels of IoT devices' architecture are clearly explained in this study where all the artificial devices are designed based on this structure. Data analysis of experiment were measured by the use of chi square test for association and research results showed the statistical significance of the estimated model and the impacts of each category over the model where we finally got accurate estimates for our research variables. This study revealed the importance of considering the students' dominant learning styles before inventing a new IoT device.

• 한국멀티미디어학회논문지
• /
• 제25권10호
• /
• pp.1512-1523
• /
• 2022

Artificial intelligence application in digital health care has been increasing with its development of artificial intelligence. The convergence of the healthcare industry and information and communication technology makes the diagnosis of diseases more simple and comprehensible. From the perspective of medical services, its practice as an initial test and a reference indicator may become widely applicable. Therefore, analyzing the factors that are the basis for existing diagnosis protocols also helps suggest directions using artificial intelligence beyond previous regression and statistical analyses. This paper conducts essential diagnostic prediction learning based on the analysis of blood cancer factors reported previously. Blood cancer diagnosis predictions based on artificial intelligence contribute to successfully achieve more than 90% accuracy and validation of blood cancer factors as an alternative auxiliary approach.

• 한국융합학회논문지
• /
• 제13권1호
• /
• pp.141-147
• /
• 2022

디지털 헬스케어의 정의는 광의로는 헬스케어 산업과 ICT가 융합되어 개인건강과 질환을 관리하는 산업영역을 의미하고, 협의로는 환자의 건강을 향상시키기 위해 의료 서비스를 관리하는데 다양한 의료 기술을 사용하는 것을 포함한다. 본 논문은 디지털 헬스케어 분야에 적용되고 있는 인공지능과 기계학습 기법들의 활용사례 소개를 통해 다양한 디지털 헬스케어 분야에 인공지능 기술이 안정적이고 효율적으로 적용할 수 있도록 설계 지침을 제공하는 데 목적이 있다. 이를 위해 본 논문에서는 의료분야와 일상생활 분야로 나누어서 살펴보았다. 두 영역은 다른 데이터 특성을 갖는다. 두 개의 영역을 보다 세분화하여 데이터 특성 및 문제 정의 및 특징에 따른 인공지능 알고리즘 활용사례를 살펴보았다. 이를 통해 디지털 헬스케어 분야에서 활용되는 인공지능 기술들에 대한 이해도를 높이고 다양한 인공지능 기술의 활용에 대한 가능성을 검토하여 인공지능 기술이 헬스케어 산업과 개인의 건강한 삶에 기여할 수 있는 근본적인 가치에 대해 고찰한다.

• 재활치료과학
• /
• 제13권2호
• /
• pp.69-84
• /
• 2024

목적 : 노인의 건강한 삶의 방식으로서 라이프스타일에 대한 연구가 증가하고 있다. 라이프스타일이 개개인의 가치와 삶의 태도를 반영하는 개념임에도 불구하고, 아직까지 개인의 어떠한 가치가 라이프스타일을 건강하게 유도하는지 파악한 연구는 부족한 실정이다. 이에 본 연구는 노인의 라이프스타일 유형을 두 가지로 분류하고, 머신러닝을 활용하여 어떠한 개인적 가치가 건강한 라이프스타일에 우선적으로 작용하는지 파악하고자 한다. 연구방법 : 본 연구는 지역사회에 거주하는 55세 이상 중고령 노인 300명을 대상으로 횡단 연구를 수행하였다. 라이프스타일은 Yonsei Lifestyle Profile-Active, Balanced, Connected, Diverse (YLP-ABCD) 응답을 사용하여 잠재프로파일 분석을 통해 유형화하였다. 라이프스타일 유형을 예측하는 개인적 가치는 YLP-V (Values) 응답을 수집하여, 예측성능이 가장 높은 머신러닝 알고리즘을 선정한 후 상대적 중요도를 파악하였다. 결과 : 잠재프로파일 분석 결과, 라이프스타일은 건강한 라이프스타일 실천형(48.87%), 비실천형(51.13%)으로 분류되었다. 실천형에 속한 중고령 노인은 비실천형에 비해 사회관계가 활발한 특성을 나타내었다. 본 연구에 포함된 머신러닝 알고리즘 중 가장 우수한 성능을 보인 모델은 서포트 벡터 머신으로, 정확도 96%, Receiver Operating Characteristic (ROC) 영역 95%로 나타났다. 본 알고리즘을 바탕으로 개인적 가치의 상대적 중요도를 분석한 결과, 건강한 식단, 건강 매체, 여가활동, 건강 제품 및 머신러닝에 주의를 기울일수록, 해당 가치에 따라 중고령 노인은 건강한 라이프스타일을 실천하는 그룹에 속할 가능성이 큰 것으로 나타났다. 결론 : 본 연구는 중고령 노인의 사회적 관계망을 포함한 건강한 라이프스타일을 유도하기 위해, 건강 식단, 매체, 여가, 제품 및 습관에 대한 가치 향상을 중점적으로 다루는 종합적인 프로그램 및 서비스의 필요성을 시사한다.

• ETRI Journal
• /
• 제44권1호
• /
• pp.135-146
• /
• 2022

The importance of big data analytics has become apparent with the increasing volume of data on the Internet. The amount of data will increase even more with the widespread use of Internet of Things (IoT). One of the most important application areas of the IoT is healthcare. This study introduces new real-time data analytics architecture for an IoT-based smart healthcare system, which consists of a wireless sensor network and a radio-frequency identification technology in a vertical domain. The proposed platform also includes high-performance data analytics tools, such as Kafka, Spark, MongoDB, and NodeJS, in a horizontal domain. To investigate the performance of the system developed, a diagnosis of Wolff-Parkinson-White syndrome by logistic regression is discussed. The results show that the proposed IoT data analytics system can successfully process health data in real-time with an accuracy rate of 95% and it can handle large volumes of data. The developed system also communicates with a riverbed modeler using Transmission Control Protocol (TCP) to model any IoT-enabling technology. Therefore, the proposed architecture can be used as a time-saving experimental environment for any IoT-based system.

• International Journal of Computer Science & Network Security
• /
• 제21권8호
• /
• pp.79-86
• /
• 2021

Alzheimer's is a chronic progressive disease which exhibits varied symptoms and behavioural traits from person to person. The deterioration in cognitive abilities is more noticeable through their Activities and Instrumental Activities of Daily Living rather than biological markers. This information discussed in social media communities was collected and features were extracted by using the proposed fuzzy logic based algorithm to address the uncertainties and imprecision in the data reported. The data thus obtained is used to train machine learning models in order to predict the longevity of the patients. Models built on features extracted using the proposed algorithm performs better than models trained on full set of features. Important findings are discussed and Support Vector Regressor with RBF kernel is identified as the best performing model in predicting the longevity of Alzheimer's patients. The results would prove to be of high value for healthcare practitioners and palliative care providers to design interventions that can alleviate the trauma faced by patients and caregivers due to chronic diseases.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권6호
• /
• pp.1518-1539
• /
• 2024

Artificial intelligence-enabled business models aim to improve decision-making, operational efficiency, innovation, and productivity. The presented systematic literature review is conducted to highlight elucidating the utilization of artificial intelligence (AI) methods and techniques within AI-enabled businesses, the significance and functions of AI-enabled organizational models and frameworks, and the design parameters employed in academic research studies within the AI-enabled business domain. We reviewed 39 empirical studies that were published between 2010 and 2023. The studies that were chosen are classified based on the artificial intelligence business technique, empirical research design, and SLR search protocol criteria. According to the findings, machine learning and artificial intelligence were reported as popular methods used for business process modelling in 19% of the studies. Healthcare was the most experimented business domain used for empirical evaluation in 28% of the primary research. The most common reason for using artificial intelligence in businesses was to improve business intelligence. 51% of main studies claimed to have been carried out as experiments. 53% of the research followed experimental guidelines and were repeatable. For the design of business process modelling, eighteen AI mythology were discovered, as well as seven types of AI modelling goals and principles for organisations. For AI-enabled business models, safety, security, and privacy are key concerns in society. The growth of AI is influencing novel forms of business.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권4호
• /
• pp.826-842
• /
• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• 한국전문물리치료학회지
• /
• 제28권3호
• /
• pp.177-185
• /
• 2021

Background: Muscle undergoes change continuously with aging. Sarcopenia, in which muscle mass decrease with aging, is associated with various diseases, the risk of falling, and the deterioration of quality of life. Obesity and sarcopenia also have a synergy effect on the disease of the older adults. Objects: This study examined the risk factors for sarcopenia, sarcopenic obesity, and sarcopenia without obesity and developed prediction models. Methods: This machine-learning study used the 2008-2011 Korea National Health and Nutrition Examination Surveys in the analysis. After data curation, 5,563 older participants were selected, of whom 1,169 had sarcopenia, 538 had sarcopenic obesity, and 631 had sarcopenia without obesity; the remaining 4,394 were normal. Decision tree and random forest models were used to identify risk factors. Results: The risk factors for sarcopenia chosen by both methods were body mass index (BMI) and duration of moderate physical activity; those for sarcopenic obesity were sex, BMI, and duration of moderate physical activity; and those for sarcopenia without obesity were BMI and sex. The areas under the receiver operating characteristic curves of all prediction models exceeded 0.75. BMI could predict sarcopenia-related disease. Conclusion: Risk factors for sarcopenia-related diseases should be identified and programs for sarcopenia-related disease prevention should be developed. Data-mining research using population data should be conducted to enhance the effectiveness of early treatment for people with sarcopenia-related diseases through predictive models.