Mining health-risk factors using PHR similarity in a hybrid P2P network

Kim, Joo-Chang; Chung, Kyungyong

doi:10.1007/s12083-018-0631-7

Cited by 34 publications

(15 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate precision, RMSE (Root Mean Square Error) based error rate is analyzed. RMSE is used to evaluate the error variation of a predictive value, and to measure a difference between an actual value and a predictive value [33,34]. The smaller RMSE value, the more precision.…”

Section: Discussionmentioning

confidence: 99%

Hybrid clustering based health decision-making for improving dietary habits

Baek

Kim

Chun

et al. 2019

THC

Self Cite

View full text Add to dashboard Cite

BACKGROUND: Humans supply a variety of nutrients to their body in dietary life, which are directly related to health. Chronic diseases are long accumulated in the body on account of heredity or living habits, and draw attention as a main issue in the era of disease-controlled longevity. Therefore, it is essential to make health care continuously through the improvement in dietary habits. OBJECTIVE: By recommending alternative food products whose diet and nutrition structure is similar to that of the food products positively influencing users' health conditions, it is possible to satisfy user's health and preference. METHOD: We used the hybrid clustering based food recommendation method that uses chronic disease based clustering, diet and nutrition ontology, diet and nutrition knowledge base. Active users are classified into the chronic disease based cluster that has the nearest euclidean distance. According to the classified clusters, food products are recommended to users, and similar food products are also recommended with the use of food clustering and knowledge base. Food products are clustered with the uses of k-means algorithm and food and nutrient data system. Based on the created food clusters and food preference data, diet and nutrition knowledge base is generated. It is composed of food cluster filter, food similarity filter, universal preference filter, and user feedback filter. The universal preference filter represents the similarity weight between diet and nutrition, and user preference. The user feedback filter has the similarity weight between active user preference and diet and nutrition. They continue to be updated through associated feedback. RESULT: The proposed health decision-making method takes into account each user's health condition so that the method has more precision than an existing recommendation method. In addition, the proposed method brings about better evaluation results than a general user-by-user health context information based recommendation method. CONCLUSION: By recommending the food products related to users' chronic diseases through the proposed hybrid clustering, it is possible to help out their healthcare. In addition, by letting users receive satisfying feedback flexibly, it is possible to improve their dietary habits.

show abstract

Section: Discussionmentioning

confidence: 99%

Hybrid clustering based health decision-making for improving dietary habits

Baek

Kim

Chun

et al. 2019

THC

Self Cite

View full text Add to dashboard Cite

show abstract

“…The differences in a level of requirement are analyzed depending on age groups (20-30 years of age group, 40-50 years or more of age group) and whether to have a disease. In the survey of nutrition management service function requirement, the items that a high requirement value in all age groups are categorized into high, middle, and low types, and the items that have a high requirement value are categorized according to whether diseases are included [25,26]. Through the meeting of experts including nutrition expert, service expert, clinical medical doctor, exercise expert, and development expert, the basic functions of nutrition management are drawn as shown in Table 4.…”

Section: Activity-based Nutrition Management Health Modelmentioning

confidence: 99%

Activity-based nutrition management model for healthcare using similar group analysis

Chung

Kim

2019

THC

Self Cite

View full text Add to dashboard Cite

BACKGROUND: Due to environmental factors such as nutrient intake imbalance, lack of exercise, and increased stress, it is necessary to control nutrition in order to prevent diseases and provide treatment in terms of healthcare. OBJECTIVE: This study proposes the activity-based nutrition management model with the use of the cluster analysis of similar group for healthcare. METHODS: The proposed method is to conduct the cluster analysis of similar group for nutrition management and to develop the real-time activity information based nutrition management algorithm with the use of big data in order to improve the quality of healthcare management service. It is to reprocess an existing nutrition database and add voice recognition function in line with the service so as to improve convenience of intake-food inputs. In addition, the Bluetooth Low Energy (BLE) communication based standard collection of bio signals occurring in real-time is developed. This study also proposes the method of improving an existing algorithm of drawing a daily recommended allowance with the use of real-time activity information, and the proposed service provides the essential information of nutrition management with the use of public big data. RESULTS: To verify the developed technology and service model and its effectiveness, the nutrition management service system is designed and developed with human interface.

show abstract

“…Test results showed a high correlation between the PSS and the stress scale. The higher the total score of PSS, the more stress [7]. The PSS index of different stress levels identifies the cut-off criteria for PSS scores and stress levels.…”

Section: Stress Management Service Using the Perceived Stress Scalementioning

confidence: 99%

Activity Recommendation Model Using Rank Correlation for Chronic Stress Management

et al. 2019

Self Cite

View full text Add to dashboard Cite

Korean people are exposed to stress due to the constant competitive structure caused by rapid industrialization. As a result, there is a need for ways that can effectively manage stress and help improve quality of life. Therefore, this study proposes an activity recommendation model using rank correlation for chronic stress management. Using Spearman’s rank correlation coefficient, the proposed model finds the correlations between users’ Positive Activity for Stress Management (PASM), Negative Activity for Stress Management (NASM), and Perceived Stress Scale (PSS). Spearman’s rank correlation coefficient improves the accuracy of recommendations by putting a basic rank value in a missing value to solve the sparsity problem and cold-start problem. For the performance evaluation of the proposed model, F-measure is applied using the average precision and recall after five times of recommendations for 20 users. As a result, the proposed method has better performance than other models, since it recommends activities with the use of the correlation between PASM and NASM. The proposed activity recommendation model for stress management makes it possible to manage user’s stress effectively by lowering the user’s PSS using correlation.

show abstract

Mining health-risk factors using PHR similarity in a hybrid P2P network

Cited by 34 publications

References 24 publications

Hybrid clustering based health decision-making for improving dietary habits

Hybrid clustering based health decision-making for improving dietary habits

Activity-based nutrition management model for healthcare using similar group analysis

Activity Recommendation Model Using Rank Correlation for Chronic Stress Management

Contact Info

Product

Resources

About