A Medical Decision Support System to Identify the Mortality Rates using ML

Deepthi N; Sushmitha V Dundaraddi; Sandeep Kumar L; Jyothsna A; Subhash K N

A Medical Decision Support System to Identify the Mortality Rates using ML

May 2024

Vol-10, Issue-3

Paper ID: 23983

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Information Science And Engineering

Keywords

Data Science Machine Learning Association Learning Mortality Rates Visual Studio SQL Server

Abstract

Patient mortality is a common occurrence in hospitals due to various factors, including the availability of resources, medical staff, and facilities. The rising rate of patient deaths is a significant concern, influenced by diseases, insufficient medical resources, and inadequate healthcare services. To address this challenge, we propose a system that automatically identifies the factors contributing to mortality rates. Our project aims to demonstrate the association between mortality and healthcare services using the ECLAT algorithm. The system will analyze the relationship between hospital resources and mortality rates using Microsoft technologies, considering parameters such as specialists, beds, ICU facilities, and nursing staff. Efficient machine learning algorithms will be employed to identify critical factors influencing mortality rates, utilizing "Visual Studio" for the front end and "SQL Server" for the backend due to their robust library and tool support for real time applications.

License

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Author Information

#	Name	Institute / Affiliation
1	Deepthi N	Vidya Vikas Institute Of Engineering And Technology
2	Sushmitha V Dundaraddi	Vidya Vikas Institute Of Engineering And Technology
3	Sandeep Kumar L	Vidya Vikas Institute Of Engineering And Technology
4	Jyothsna A	Vidya Vikas Institute Of Engineering And Technology
5	Subhash K N	Vidya Vikas Institute Of Engineering And Technology

How to Cite

Use the following formats to cite this article in your research.

APA Style

N, Deepthi, Dundaraddi, Sushmitha V, L, Sandeep Kumar, A, Jyothsna, & N, Subhash K (2024). A Medical Decision Support System to Identify the Mortality Rates using ML. International Journal of Advance Research and Innovative Ideas In Education, 10(3), 2503-2507.

MLA Style

N, Deepthi, et al. "A Medical Decision Support System to Identify the Mortality Rates using ML." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 3, 2024, pp. 2503-2507.

IEEE Style

Deepthi N, Sushmitha V Dundaraddi, Sandeep Kumar L, Jyothsna A, and Subhash K N, "A Medical Decision Support System to Identify the Mortality Rates using ML," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 3, pp. 2503-2507, 2024.

Vancouver Style

N Deepthi, Dundaraddi Sushmitha V, L Sandeep Kumar, A Jyothsna, N Subhash K. A Medical Decision Support System to Identify the Mortality Rates using ML. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(3):2503-2507.

Harvard Style

N, Deepthi, Dundaraddi, Sushmitha V, L, Sandeep Kumar, A, Jyothsna, & N, Subhash K (2024) 'A Medical Decision Support System to Identify the Mortality Rates using ML', International Journal of Advance Research and Innovative Ideas In Education, 10(3), pp. 2503-2507.

Chicago Style

N, Deepthi, et al. "A Medical Decision Support System to Identify the Mortality Rates using ML." International Journal of Advance Research and Innovative Ideas In Education 10, no. 3 (2024): 2503-2507.

Turabian Style

N, Deepthi, et al. "A Medical Decision Support System to Identify the Mortality Rates using ML." International Journal of Advance Research and Innovative Ideas In Education 10, no. 3 (2024): 2503-2507.