scholarly journals A machine learning approach to identify and prioritize college students at risk of dropping out

2017 ◽  
Author(s):  
Artur Barbosa ◽  
Emanuele Santos ◽  
João Paulo Pordeus
Keyword(s):  
Machine Learning ◽  
College Students ◽  
At Risk ◽  
Dropping Out ◽  
Learning Approach ◽  
Students At Risk ◽  
Machine Learning Approach

scholarly journals A Machine Learning Approach for Identifying Children at Risk of Suboptimal Adherence to Growth Hormone Therapy

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A672-A673
Author(s):  
Amalia Spataru ◽  
Paula van Dommelen ◽  
Lilian Arnaud ◽  
Quentin Le Masne ◽  
Silvia Quarteroni ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Growth Hormone ◽  
Clinical Outcomes ◽  
Growth Disorders ◽  
Learning Approach ◽  
Increased Risk ◽  
Machine Learning Approach ◽  
Suboptimal Adherence ◽  
Optimal Adherence

Abstract Background: Suboptimal adherence to recombinant human growth hormone (r-hGH) treatment can lead to suboptimal clinical outcomes. Being able to identify children who are at risk of suboptimal adherence in the near future, and take adequate measures to support adherence, may maximize clinical outcomes. Our aim was to develop a model based on data from the first 3 months of treatment to identify potential indicators of suboptimal adherence and predict adherence over the following 9 months using a machine learning approach. Methods: We assessed adherence to r-hGH treatment in children with growth disorders in their first 12 months of treatment using a connected autoinjector and e-device (easypod™), which automatically transmits adherence data via an online portal (easypod™ connect). We selected children who started the use of the device before 18 years of age and who transmitted their injection data for at least 12 months. Adherence (mg injected/mg prescribed) between 4-12 months (outcome) was categorized as optimal (≥85%) versus suboptimal (<85%). In addition to adherence over the first 3 months, comfort settings (needle speed, injection depth, injection speed, injection time), number of transmissions, number of dose changes, age at start and sex were used as potential indicators of suboptimal adherence. Several machine learning models were optimized on a class-balanced training dataset using a 5-fold cross-validation scheme. On the best performing model, machine learning interpretation techniques and chi-squared statistical tests were applied to extract the statistically significant indicators of suboptimal and optimal adherence. Results: Anonymized data were available for 10,943 children. The optimal prediction performances were achieved with the random forest algorithm. The mean adherence and the adherence standard deviation over the first 3 months were the two most important features for predicting adherence in the following 9 months. Not using the system’s features (e.g. not transmitting data often and not changing some of the comfort settings, such as the needle speed setting), as well as starting treatment at an older age were significantly associated with an increased risk of suboptimal adherence (p<0.001). When tested on first-time seen data following the same class distribution as the original data, the model achieved a sensitivity of 80% and a specificity of 81%. Conclusions: We developed a model predicting whether a child’s adherence in the following 9 months will be below or above the optimal threshold (85%) based on early data from the first 3 months of treatment and we identified the indicators of suboptimal adherence. These results can be used to identify children needing additional medical or other support to reach optimal adherence and therefore optimal clinical outcomes.

scholarly journals Estimate of the HOMA-IR Cut-off Value Identifying Subjects at Risk of Insulin Resistance using a Machine Learning Approach

2021 ◽  
Author(s):  
Abdelhamid Abdessalem ◽  
Hamza Zidoum ◽  
Fahd Zadjali ◽  
Rachid Hedjam ◽  
Aliya Al-Ansari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Insulin Resistance ◽  
At Risk ◽  
Clinical Data ◽  
Clustering Algorithm ◽  
Early Stage ◽  
Strong Relationship ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Research Findings

Objective: This paper describes an unsupervised Machine Learning approach to estimate the HOMA-IR cut-off identifying subjects at risk of insulin resistance in a given ethnic group, based on the clinical data of a representative sample. Methods: We apply the approach to clinical data of individuals of Arab ancestors obtained from a family study conducted in the city of Nizwa between January 2000 and December 2004. First, we identify HOMA-IR-correlated variables to which we apply our own clustering algorithm. Two clusters having the smallest overlap in their HOMA-IR values are returned. These clusters represent samples of two populations: insulin sensitive subjects and individuals at risk of insulin resistance. The cut-off value is estimated from intersections of the Gaussian functions modelling the HOMA-IR distributions of these populations. Results: We identified a HOMA-IR cut-off value of 1.62+/-0.06. We demonstrated the validity of this cut-off by 1) Showing that clinical characteristics of the identified groups match well published research findings about insulin resistance. 2) Showing a strong relationship between the segmentations resulting from the proposed cut-off and that resulting from the 2-hours glucose cut-off recommended by WHO for detecting prediabetes. Finally, we showed that the method is also able to identify cut-off values for similar problems (e.g. fasting sugar cut-off for prediabetes). Conclusion: The proposed method defines a HOMA-IR cut-off value for detecting individuals at risk of insulin resistance. Such method can identify high risk individuals at early stage which may prevent or at least delay the onset of chronic diseases like type 2 diabetes. Keywords: Machine Learning; Feature Selection; K-mean++ Clustering; Insulin Resistance; HOMA-IR; T2DM.

scholarly journals Prediction of adolescent subjective well-being: A machine learning approach

2019 ◽  
Vol 32 (5) ◽  
pp. e100096
Author(s):  
Naixin Zhang ◽  
Chuanxin Liu ◽  
Zhixuan Chen ◽  
Lin An ◽  
Decheng Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Undergraduate Students ◽  
Online Survey ◽  
Well Being ◽  
Gradient Boosting ◽  
Control Procedure ◽  
Learning Approach ◽  
Subjective Well Being ◽  
Machine Learning Approach

BackgroundSubjective well-being (SWB), also known as happiness, plays an important role in evaluating both mental and physical health. Adolescents deserve specific attention because they are under a great variety of stresses and are at risk for mental disorders during adulthood.AimThe present paper aims to predict undergraduate students’ SWB by machine learning method.MethodsGradient Boosting Classifier which was an innovative yet validated machine learning approach was used to analyse data from 10 518 Chinese adolescents. The online survey included 298 factors such as depression and personality. Quality control procedure was used to minimise biases due to online survey reports. We applied feature selection to achieve the balance between optimal prediction and result interpretation.ResultsThe top 20 happiness risks and protective factors were finally brought into the predicting model. Approximately 90% individuals’ SWB can be predicted correctly, and the sensitivity and specificity were about 92% and 90%, respectively.ConclusionsThis result identifies at-risk individuals according to new characteristics and established the foundation for adolescent prevention strategies.

scholarly journals Social Media Markers to Identify Fathers at Risk of Postpartum Depression: A Machine Learning Approach

2020 ◽  
Vol 23 (9) ◽  
pp. 611-618 ◽  
Author(s):  
Adrian B.R. Shatte ◽  
Delyse M. Hutchinson ◽  
Matthew Fuller-Tyszkiewicz ◽  
Samantha J. Teague
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Social Media ◽  
Postpartum Depression ◽  
Learning Approach ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document