AI-Enhanced Clinical Decision Support Systems with Neural Networking
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© 2024 by IJCTT Journal
Volume-72 Issue-2
Year of Publication : 2024
Authors : Naveen Vemuri, Naresh Thaneeru, Ezekiel Nnamere Aneke, Venkata Manoj Tatikonda
DOI :  10.14445/22312803/IJCTT-V72I2P110

How to Cite?

Naveen Vemuri, Naresh Thaneeru, Ezekiel Nnamere Aneke, Venkata Manoj Tatikonda, "AI-Enhanced Clinical Decision Support Systems with Neural Networking," International Journal of Computer Trends and Technology, vol. 72, no. 2, pp. 56-60, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I2P110

Abstract
The health sector has experienced a clear paradigm shift in the past couple of years, especially in using groundbreaking technologies such as machine learning, artificial intelligence, etc. This paper’s primary goal is to show how patients diagnose their medical condition based on different parameters, including age, BMI, blood pressure, pulse rate, respiration rate, body temperature, CBC, lipid profile, and blood glucose. The results were used to categorize patients into three levels based on the intensity of their fluctuations: level 1, level 2, and level 3. Medical experts can use such an analysis as a preliminary diagnosis for treatment, depending on whether other methods are required. The research incorporates developing two models, one deployed with LSTM-The model and another with deep learning. While both techniques proved their efficiency, the LSTM model received a higher accuracy score, evaluated by the root mean squared error scores.

Keywords
Artificial Intelligence, Deep Learning, Healthcare, LSTM, Neural Network.

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