Naidile S Saragodu, Shreedhara N Hegde, Harprith Kaur
The goal of this promising area of study is to enhance prenatal care and lower fetal morbidity and mortality by utilizingmachine learning to anticipate fetal disease. In this study, we present a machine learning-based strategy for predicting fetaldiseases from clinical data. First, we gathered a sizable collection of clinical information from expectant mothers with various fetal disorders. Using clinical guidelines, we pre-processed the data and retrieved pertinent features. We integrated a range of machine learning algorithms, including logistic regression, support vector machines, decision trees, and random forests, to train and test our model. We evaluated the performance of our model using severalfactors, such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC).The results of this study demonstrate how machine learning algorithms can accurately forecast fetal health status. The developed models achieve good accuracy and AUC-ROC ratings todistinguish between healthy and at-risk fetuses. The interpretability study identifies key clinical characteristics that have a significant impact on the prediction, providing medical practitioners with useful information when making decisions about prenatal care. Through the provision of more unbiasedand precise assessments of fetal health status, machine learning techniques incorporated into prenatal care have the potential to transform the industry. By providing accurate and early projections, this technology can assist healthcare professionals in identifying high-risk pregnancies and carrying out the necessary procedures, improving mother and fetal outcomes. Future research should concentrate on verifying and improving predictive models on larger and more varied datasets to ensure real-world applicability and reliability
{"title":"Prediction of Fetal Health Status Using Machine Learning","authors":"Naidile S Saragodu, Shreedhara N Hegde, Harprith Kaur","doi":"10.61453/jods.v2024no17","DOIUrl":"https://doi.org/10.61453/jods.v2024no17","url":null,"abstract":"The goal of this promising area of study is to enhance prenatal care and lower fetal morbidity and mortality by utilizingmachine learning to anticipate fetal disease. In this study, we present a machine learning-based strategy for predicting fetaldiseases from clinical data. First, we gathered a sizable collection of clinical information from expectant mothers with various fetal disorders. Using clinical guidelines, we pre-processed the data and retrieved pertinent features. We integrated a range of machine learning algorithms, including logistic regression, support vector machines, decision trees, and random forests, to train and test our model. We evaluated the performance of our model using severalfactors, such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC).The results of this study demonstrate how machine learning algorithms can accurately forecast fetal health status. The developed models achieve good accuracy and AUC-ROC ratings todistinguish between healthy and at-risk fetuses. The interpretability study identifies key clinical characteristics that have a significant impact on the prediction, providing medical practitioners with useful information when making decisions about prenatal care. Through the provision of more unbiasedand precise assessments of fetal health status, machine learning techniques incorporated into prenatal care have the potential to transform the industry. By providing accurate and early projections, this technology can assist healthcare professionals in identifying high-risk pregnancies and carrying out the necessary procedures, improving mother and fetal outcomes. Future research should concentrate on verifying and improving predictive models on larger and more varied datasets to ensure real-world applicability and reliability","PeriodicalId":15636,"journal":{"name":"Journal of data science","volume":"5 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.
{"title":"Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations","authors":"JooYoung Seo, Mine Dogucu","doi":"10.6339/23-jds1095","DOIUrl":"https://doi.org/10.6339/23-jds1095","url":null,"abstract":"Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.","PeriodicalId":15636,"journal":{"name":"Journal of data science","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135585729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the form of a scholarly exchange with ChatGPT, we cover fundamentals of modeling stochastic dependence with copulas. The conversation is aimed at a broad audience and provides a light introduction to the topic of copula modeling, a field of potential relevance in all areas where more than one random variable appears in the modeling process. Topics covered include the definition, Sklar’s theorem, the invariance principle, pseudo-observations, tail dependence and stochastic representations. The conversation also shows to what degree it can be useful (or not) to learn about such concepts by interacting with the current version of a chatbot.
{"title":"The Philosophy of Copula Modeling: A Conversation with ChatGPT","authors":"Marius Hofert","doi":"10.6339/23-jds1114","DOIUrl":"https://doi.org/10.6339/23-jds1114","url":null,"abstract":"In the form of a scholarly exchange with ChatGPT, we cover fundamentals of modeling stochastic dependence with copulas. The conversation is aimed at a broad audience and provides a light introduction to the topic of copula modeling, a field of potential relevance in all areas where more than one random variable appears in the modeling process. Topics covered include the definition, Sklar’s theorem, the invariance principle, pseudo-observations, tail dependence and stochastic representations. The conversation also shows to what degree it can be useful (or not) to learn about such concepts by interacting with the current version of a chatbot.","PeriodicalId":15636,"journal":{"name":"Journal of data science","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Racial and ethnic representation in home ownership rates is an important public policy topic for addressing inequality within society. Although more than half of the households in the US are owned, rather than rented, the representation of home ownership is unequal among different racial and ethnic groups. Here we analyze the US Census Bureau’s American Community Survey data to conduct an exploratory and statistical analysis of home ownership in the US, and find sociodemographic factors that are associated with differences in home ownership rates. We use binomial and beta-binomial generalized linear models (GLMs) with 2020 county-level data to model the home ownership rate, and fit the beta-binomial models with Bayesian estimation. We determine that race/ethnic group, geographic region, and income all have significant associations with the home ownership rate. To make the data and results accessible to the public, we develop an Shiny web application in R with exploratory plots and model predictions.
{"title":"Exploring Racial and Ethnic Differences in US Home Ownership with Bayesian Beta-Binomial Regression","authors":"Jhonatan Medri, Tejasvi Channagiri, Lu Lu","doi":"10.6339/23-jds1113","DOIUrl":"https://doi.org/10.6339/23-jds1113","url":null,"abstract":"Racial and ethnic representation in home ownership rates is an important public policy topic for addressing inequality within society. Although more than half of the households in the US are owned, rather than rented, the representation of home ownership is unequal among different racial and ethnic groups. Here we analyze the US Census Bureau’s American Community Survey data to conduct an exploratory and statistical analysis of home ownership in the US, and find sociodemographic factors that are associated with differences in home ownership rates. We use binomial and beta-binomial generalized linear models (GLMs) with 2020 county-level data to model the home ownership rate, and fit the beta-binomial models with Bayesian estimation. We determine that race/ethnic group, geographic region, and income all have significant associations with the home ownership rate. To make the data and results accessible to the public, we develop an Shiny web application in R with exploratory plots and model predictions.","PeriodicalId":15636,"journal":{"name":"Journal of data science","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135313124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In 2022 the American Statistical Association established the Riffenburgh Award, which recognizes exceptional innovation in extending statistical methods across diverse fields. Simultaneously, the Department of Statistics at the University of Connecticut proudly commemorated six decades of excellence, having evolved into a preeminent hub for academic, industrial, and governmental statistical grooming. To honor this legacy, a captivating virtual dialogue was conducted with the department’s visionary founder, Dr. Robert H. Riffenburgh, delving into his extraordinary career trajectory, profound insights into the statistical vocation, and heartfelt accounts from the faculty and students he personally nurtured. This multifaceted narrative documents the conversation with more detailed background information on each topic covered by the interview than what is presented in the video recording on YouTube.
{"title":"Legendary Career and Colorful Life: A Conversation with Dr. Bob Riffenburgh","authors":"Haim Bar, Jun Yan","doi":"10.6339/23-jds1115","DOIUrl":"https://doi.org/10.6339/23-jds1115","url":null,"abstract":"In 2022 the American Statistical Association established the Riffenburgh Award, which recognizes exceptional innovation in extending statistical methods across diverse fields. Simultaneously, the Department of Statistics at the University of Connecticut proudly commemorated six decades of excellence, having evolved into a preeminent hub for academic, industrial, and governmental statistical grooming. To honor this legacy, a captivating virtual dialogue was conducted with the department’s visionary founder, Dr. Robert H. Riffenburgh, delving into his extraordinary career trajectory, profound insights into the statistical vocation, and heartfelt accounts from the faculty and students he personally nurtured. This multifaceted narrative documents the conversation with more detailed background information on each topic covered by the interview than what is presented in the video recording on YouTube.","PeriodicalId":15636,"journal":{"name":"Journal of data science","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136301956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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