Fuzzy Near Compactness Based Personalized Recommendation for Preventing Patients with Type 2 Diabetes Mellitus and Hypertension from Cardiovascular Complication

Rachata, Napa; Rueangsirarak, Worasak; Kamyod, Chayapol; Temdee, Punnarumol

doi:10.1007/s11277-020-07532-7

Cited by 1 publication

(2 citation statements)

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“…Recommendation systems can reduce the time required to explore the needed data [23,39]. In healthcare services, many healthcare providers have applied personalized recommendation systems to understand patients' health conditions and suggest personalized healthcare services to each person [41][42][43][44]. Personalized recommendation systems can empower patients' self-monitoring by notifying them of and suggesting appropriate individual healthcare programs, such as medical services and treatment plans [41][42][43].…”

Section: Personalized Recommendations In Healthcarementioning

confidence: 99%

“…Health data include measurements related to health conditions, such as BMI, heart rate, and blood pressure. Lifestyle data typically include habits, hobbies, and consumption and exercise behaviors [41][42][43][44]. For exercise applications, personalized recommendation systems provide individual programs to help users properly manage their daily life activities [44].…”

Section: Personalized Recommendations In Healthcarementioning

confidence: 99%

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Context-aware Based Personalized Recommendation on Mobile for Monitoring Excessive Sugar Consumption of Thai Adolescents

Suwan

Temdee

Prasad

2022

JMM

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Given the harmful effects of excessive sugar consumption, everyone should be aware of the amount of sugar they consume in daily life. Generally, managing nutrition and preventing negative effects from sugar intake requires a nutritional specialist or specialized knowledge, which may not always be readily accessible or understandable. Therefore, there is a need for a mobile application that can track excessive sugar consumption. This study proposes a context-aware personalized recommendation mobile application for monitoring excessive sugar consumption and providing individual recommendations (based on a predefined set of 144 rules) to Thai adolescents. The application is user friendly and can be used to provide recommendations to users for sugar consumption and proper exercise each day. Personal, health, and lifestyle data are collected and analyzed to provide individualized recommendations to each user. Experiments were conducted with 140 Thai adolescents aged 15 to 25 years old. Users’ preferences regarding degree of awareness in presentation style were also investigated. Users rated their satisfaction with the proposed mobile application as very high in terms of both function and personalization. In addition, fully automated recommendations were found to be the preferred degree of awareness among the test group.

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