scholarly journals A comparative study of machine learning techniques for suicide attempts predictive model

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822198939
Author(s):  
Noratikah Nordin ◽  
Zurinahni Zainol ◽  
Mohd Halim Mohd Noor ◽  
Chan Lai Fong
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Predictive Model ◽  
Predictive Models ◽  
Suicide Ideation ◽  
Suicide Attempts ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Recursive Feature Elimination

Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts.

Abstract 122: Use of Machine Learning to Accurately Predict Adverse Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Elsie G Ross ◽  
Nicholas Leeper ◽  
Nigam Shah
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Predictive Model ◽  
Predictive Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Record ◽  
Peripheral Artery ◽  
Artery Disease

Introduction: Patients with peripheral artery disease (PAD) are at high risk of major adverse cardiac and cerebrovascular events (MACCE). However, no currently available risk scores accurately delineate which patients are most likely to sustain an event, creating a missed opportunity for more aggressive risk factor management. We set out to develop a novel predictive model - based on automated machine learning algorithms using electronic health record (EHR) data - with the aim of identifying which PAD patients are most likely to have an adverse outcome during follow-up. Methods: Data were derived from patients with a diagnosis of PAD at our institution. Novel machine-learning algorithms including random forest and penalized regression predictive models were developed using structured and unstructured data that including lab values, diagnosis codes, medications, and clinical notes. Patients were matched for total follow-up time to remove length of patient records as a biasing factor in our predictive models. Results: After matching for length of follow-up, 3,807 patients were included in our models. A total of 1,269 patients had a MACCE event after PAD diagnosis. The median time to MACCE was 2.8 years after PAD diagnosis. Utilizing 1,492 different variables extracted from the EHR, our best predictive model was able to very accurately predict which patients would go on to have a MACCE event after diagnosis of PAD with an AUC of 0.98, with a sensitivity, specificity and positive predictive value of 0.90, 0.96, and 0.93, respectively. Conclusions: Hypothesis-free, machine-learning algorithms using freely available data in the EHR can accurately predict which PAD patients are most likely to go on to develop future MACCE. While these findings require validation in an independent data set, there is hope that these informatics approaches can be applied to the medical record in an automated fashion to risk stratify patients with vascular disease and identify those who might benefit from more aggressive disease management in real-time.

COMPARATIVE STUDY OF VARIOUS MACHINE LEARNING ALGORITHMS FOR TWEET CLASSIFICATION

2019 ◽  
Vol 6 (4) ◽  
pp. 12
Author(s):  
ABUBAKAR UMAR ◽  
A. BASHIR SULAIMON ◽  
BASHIR ABDULLAHI MUHAMMAD ◽  
S. ADEBAYO OLAWALE ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Tweet Classification

scholarly journals Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1089
Author(s):  
Sung-Hee Kim ◽  
Chanyoung Jeong
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Learning Techniques ◽  
Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

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