RETRACTED ARTICLE: A classification model to predict onset of smoking and drinking habits based on socio-economic and sociocultural factors

Abirami, M.; Vennila, B.; Chilukalapalli, Eesh L.; Kuriyedath, Rahul

doi:10.1007/s12652-020-01796-4

Cited by 7 publications

(2 citation statements)

References 21 publications

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“… Prediction Survey Spain 5381 Social vulnerability ANNs a , decision tree There is a connection and relationship between demographic and social vulnerability phenomena and the residential configuration of Andalusia. ( Abirami et al, 2020 ) 2020 To analyze how socio-economic and socio-cultural factors play a role in the initiation and cultivation of addictive behaviors and use a machine learning approach to predict the early onset of such behaviors. Prediction Survey Global 176 Smoking and alcohol habit Gaussian naïve Bayes, SVM a , logistic regression algorithms Logistic Regression to be the best performing classifier to predict both drinking and smoking habits.…”

Section: Methodsmentioning

confidence: 99%

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Kino

Hsu

Shiba

et al. 2021

SSM - Population Health

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Background Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH). Methods Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report. Results Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness). Conclusions While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

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Section: Methodsmentioning

confidence: 99%

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Kino

Hsu

Shiba

et al. 2021

SSM - Population Health

View full text Add to dashboard Cite

show abstract

“…Road accidents resulted in severe injuries and the loss of human lives, animals, and property. Road accidents kill 1.3 million people per year, with an average of 3,287 deaths per day and between 20 and 50 million wounded or incapacitated [28]. World Health Organization, report estimated yearly majority people die in road accidents, between the ages of 19 and 29 [5].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Model for Avoiding Road Accidents Using Artificial Neural Network

Kushwaha,

M.S. Abirami

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Accidents typically occurred on roads, resulting in significant societal losses. Road accidents are a worldwide issue that result in the loss of precious human lives and property. The purpose of this paper is to create an intelligent system-based on Machine Learning model for avoiding road accidents, as well as a system that effectively reduces road accidents severities. The Artificial Neural Network (ANN) algorithm, along with others such as Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Nave Bayes (NB), Stochastic Gradient Descent (SGD), Random Forest (RF), Gradient Boosting (GB), and AdaBoost, is used to create an intelligence system. Many driving collaborator procedures, installed in a few vehicles, assist drivers in avoiding vehicle crashes by providing early cautioning messages. The intelligence road crash avoidance system model is built on dataset of 29 columns and 1048575 rows. Preprocessing, feature selection, and feature extraction performed with the help of heat map and correlation matrix are used to select features. Linear Discriminant Analysis (LDA) is used for feature extraction. The testing dataset revealed that the proposed ANN method outperforms other algorithms with an accuracy of 0.856. Intelligent systems aid in the prevention of traffic accidents, which aids police officers and researchers in developing new policies.

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Inferred Networks and the Social Determinants of Health

Sanjel

Matta

2022

Complex Networks &Amp; Their Applications X

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RETRACTED ARTICLE: A classification model to predict onset of smoking and drinking habits based on socio-economic and sociocultural factors

Cited by 7 publications

References 21 publications

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Intelligent Model for Avoiding Road Accidents Using Artificial Neural Network

Inferred Networks and the Social Determinants of Health

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