The Analysis of Machine Learning Techniques for Heart Disease Prediction

Singh, Surendra Pal; Shrivastava, Vishal

doi:10.1007/978-981-15-5077-5_12

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…Other related works pertaining to big data and data analytics for predicting and monitoring of health conditions and diseases include Alqaissi et al (2022), Bansal et al (2016), Catalyze (2022), Corsi et al (2020), Foresee Medical (2022, National Academy of Medicine (2016), Roy et al (2021), Singh et al (2021), Sweeney et al (2023), Venkatesh et al (2019), Yadav et al (2021) and Zhang (2020).…”

Section: Research Objectivesmentioning

confidence: 99%

Data Visualization of Big Data for Predictive and Descriptive Analytics for Stroke, COVID-19, and Diabetes

Segall,

Takashashi

2023

International Journal of Big Data and Analytics in Healthcare

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Visualization of big data is crucial for meaningful interpretations and especially for healthcare. Brief discussions are made for big data, background for healthcare, and recent work in big data analytics for healthcare. This research pertains to different levels of big data: 5,110 vs. 101,766 vs. 320,200 vs. 1 million data values. Data visualizations and predictive analytics are presented of big data for selected diseases of stroke with 5,110 data values, diabetes with 101,766 data values, and two COVID-19 studies: one with 320,200 data values and another with 1 million data values. Data visualizations are generated for these diseases with big data using Tableau. For stroke patients, an investigation was performed to determine how different living environments affect relationship between strokes. The data visualizations for diabetes showed impact of insulin use yielded reduced hospital stays. Data visualizations for COVID-19 provided temporal trends in confirmed cases, mortality, and recovery rates for 2020-2023. Conclusions and future directions of research are presented.

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