Usage of Nearest Neighborhood, Decision Tree and Bayesian Classification Techniques in Development of Weight Management Counseling System

2008 ◽  
Author(s):  
Sunita Soni ◽  
Jyothi Pillai
Keyword(s):  
Decision Tree ◽  
Weight Management ◽  
Bayesian Classification ◽  
Classification Techniques ◽  
Weight Management Counseling ◽  
Nearest Neighborhood

scholarly journals Teaching Medical Students to Help Patients Manage Their Weight: Outcomes of an Eight-School Randomized Controlled Trial

2021 ◽  
Author(s):  
Judith K. Ockene ◽  
Lori Pbert ◽  
Sybil Crawford ◽  
Christine F. Frisard ◽  
Jyothi A. Pendharkar ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Medical Students ◽  
Weight Management ◽  
Management Education ◽  
Controlled Trial ◽  
Health Behavior Change ◽  
Medical Schools ◽  
Randomized Controlled ◽  
Weight Management Counseling ◽  
Overweight Or Obesity

ABSTRACT BACKGROUND Given the rising rates of obesity there is a pressing need for medical schools to better prepare students for intervening with patients who have overweight or obesity and for prevention efforts. OBJECTIVE To assess the effect of a multi-modal weight management curriculum on counseling skills for health behavior change. DESIGN A pair-matched, group-randomized controlled trial (2015-2020) included students enrolled in eight U.S. medical schools randomized to receive either multi-modal weight management education (MME) or traditional weight management education (TE). SETTING/PARTICIPANTS Students from the class of 2020 (N=1305) were asked to participate in an objective structured clinical examination (OSCE) focused on weight management counseling and complete pre and post surveys. A total of 70.1% of eligible students (N=915) completed the OSCE and 69.3% (N=904) completed both surveys. INTERVENTIONS: The MME implemented over three years included a web-based course, a role-play classroom exercise, a web-patient encounter with feedback, and an enhanced clerkship experience with preceptors trained in weight management counseling (WMC). Counseling focused on the 5As (Ask, Advise, Assess, Assist, Arrange) and patient-centeredness. MEASUREMENTS The outcome was student 5As WMC skills assessed using an objective measure, an OSCE, scored using a behavior checklist, and a subjective measure, student self-reported skills for performing the 5As. RESULTS Among MME students who completed two of three WMC components compared to those who completed none, exposure was significantly associated with higher OSCE scores and self-reported 5A skills. LIMITATIONS Variability in medical schools requiring participation in the WMC curriculum. CONCLUSIONS This trial revealed that medical students struggle with delivering weight management counseling to their patients who have overweight or obesity. Medical schools, though restrained in adding curricula, should incorporate should incorporate multiple WMC curricula components early in medical student education to provide knowledge and build confidence for supporting patients in developing individualized plans for weight management. NIH Trial Registry Number R01-194787

A comparative study to recognize fake ratings in recommendation system using classification techniques

2021 ◽  
pp. 1-8
Author(s):  
P. Shanmuga Sundari ◽  
M. Subaji
Keyword(s):  
Decision Tree ◽  
False Positive ◽  
Recommendation System ◽  
Target Item ◽  
User Profile ◽  
Method Performance ◽  
Classification Techniques ◽  
Attack Model ◽  
Unique Method ◽  
Decision Tree Method

The recommendation system is affected with attacks when the users are given liberty to rate the items based on their impression about the product or service. Some malicious user or other competitors’ try to inject fake rating to degrade the item’s graces that are mostly adored by several users. Attacks in the rating matrix are not executed just by a single profile. A group of users profile is injected into rating matrix to decrease the performance. It is highly complex to extract the fake ratings from the mixture of genuine profile as it resides the same pattern. Identifying the attacked profile and the target item of the fake rating is a challenging task in the big data environment. This paper proposes a unique method to identify the attacks in collaborating filtering method. The process of extracting fake rating is carried out in two phases. During the initial phase, doubtful user profile is identified from the rating matrix. In the following phase, the target item is analysed using push attack count to reduce the false positive rates from the doubtful user profile. The proposed model is evaluated with detection rate and false positive rates by considering the filler size and attacks size. The experiment was conducted with 6%, 8% and 10% filler sizes and with different attack sizes that ranges from 0%–100%. Various classification techniques such as decision tree, logistic regression, SVM and random forest methods are used to classify the fake ratings. From the results, it is witnessed that SVM model works better with random and bandwagon attack models at an average of 4% higher accuracy. Similarly the decision tree method performance better at an average of 3% on average attack model.

scholarly journals Students’ Report of Preceptor Weight Management Counseling at Eight U.S. Medical Schools

2018 ◽  
Vol 55 (5) ◽  
pp. e139-e145 ◽  
Author(s):  
Alan C. Geller ◽  
Judith K. Ockene ◽  
Mukti Kulkarni ◽  
Linda C. Churchill ◽  
Christine F. Frisard ◽  
...  
Keyword(s):  
Weight Management ◽  
Medical Schools ◽  
Weight Management Counseling

Development of a six-factor questionnaire for use in weight management counseling

2016 ◽  
Vol 99 (12) ◽  
pp. 2018-2025 ◽  
Author(s):  
Robert F. Kushner ◽  
Seung W. Choi ◽  
James L. Burns
Keyword(s):  
Weight Management ◽  
Weight Management Counseling

Primary Care Providers Welcome Smartphone Apps That Assist in Pediatric Weight Management

2019 ◽  
Vol 58 (6) ◽  
pp. 665-670 ◽  
Author(s):  
Christine B. SanGiovanni ◽  
Kristen Morella ◽  
James Russell Roberts
Keyword(s):  
Primary Care ◽  
Weight Management ◽  
Primary Care Providers ◽  
Obese Patients ◽  
Smartphone Apps ◽  
Care Providers ◽  
Use Of Resources ◽  
Weight Management Counseling ◽  
Pediatric Weight Management ◽  
Pediatric Providers

Primary care providers (PCPs) have few resources to manage their overweight/obese patients. The purpose of study was to determine if technological resources, such as smartphone apps, may be useful for weight management counseling. PCPs were surveyed about their current use of resources for obesity management and whether smartphone apps would be helpful. Seventy-four PCPs completed the survey. Only 15% currently referred patients to smartphone apps at least sometimes or more often. When asked about features of apps, 66% were not aware of apps with interactive healthy eating games, yet if aware, 45% reported they would refer patients. Providers reported a greater likelihood of being more effective using an app compared with their current ability to manage overweight/obesity, 3.19 versus 2.85, P < .01. The majority of pediatric providers surveyed do not provide technological resources for their overweight/obese patients; yet, they appear interested in using smartphone apps as a resource.

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