How to Design an Interactive System for Data Science: Learning from a Literature Review

ObjectiveThe purpose of this research is to identify how data science is applied in suicide prevention literature, describe the current landscape of this literature and highlight areas where data science may be useful for future injury prevention research.DesignWe conducted a literature review of injury prevention and data science in April 2020 and January 2021 in three databases.MethodsFor the included 99 articles, we extracted the following: (1) author(s) and year; (2) title; (3) study approach (4) reason for applying data science method; (5) data science method type; (6) study description; (7) data source and (8) focus on a disproportionately affected population.ResultsResults showed the literature on data science and suicide more than doubled from 2019 to 2020, with articles with individual-level approaches more prevalent than population-level approaches. Most population-level articles applied data science methods to describe (n=10) outcomes, while most individual-level articles identified risk factors (n=27). Machine learning was the most common data science method applied in the studies (n=48). A wide array of data sources was used for suicide research, with most articles (n=45) using social media and web-based behaviour data. Eleven studies demonstrated the value of applying data science to suicide prevention literature for disproportionately affected groups.ConclusionData science techniques proved to be effective tools in describing suicidal thoughts or behaviour, identifying individual risk factors and predicting outcomes. Future research should focus on identifying how data science can be applied in other injury-related topics.

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A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics

Advances in Data Mining and Database Management - Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics ◽

10.4018/978-1-7998-3053-5.ch021 ◽

2021 ◽

pp. 416-428

Author(s):

R. Suganya ◽

Rajaram S. ◽

Kameswari M.

Keyword(s):

Machine Learning ◽

Literature Review ◽

Data Science ◽

Learning Algorithms ◽

Research Literature ◽

Machine Learning Algorithms ◽

Thyroid Disorder ◽

Classification Models ◽

Indian Women ◽

Using Data

Currently, thyroid disorders are more common and widespread among women worldwide. In India, seven out of ten women are suffering from thyroid problems. Various research literature studies predict that about 35% of Indian women are examined with prevalent goiter. It is very necessary to take preventive measures at its early stages, otherwise it causes infertility problem among women. The recent review discusses various analytics models that are used to handle different types of thyroid problems in women. This chapter is planned to analyze and compare different classification models, both machine learning algorithms and deep leaning algorithms, to classify different thyroid problems. Literature from both machine learning and deep learning algorithms is considered. This literature review on thyroid problems will help to analyze the reason and characteristics of thyroid disorder. The dataset used to build and to validate the algorithms was provided by UCI machine learning repository.

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