Vol. 2024 (2024)
Articles

Healthcare Analysis Based on Diabetes Prediction Using a Cuckoo-Based Deep Convolutional Long-Term Memory Algorithm

T. Kavitha
Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu 600062, India .
G. Amirthayogam
Department of Information Technology, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu 603103, India.
J. Jasmine Hephzipah
Department of Electronics and Communication Engineering, R.M.K. Engineering College, Kavaraipettai, Tamil Nadu 601206, India,
R. Suganthi
Department of Electronics and Communication Engineering, Panimalar Engineering college Poonamallee, Chennai-600123, Tamil Nadu, India.
Venkata Anjani Kumar G
Department of EEE, Rajiv Gandhi University of Knowledge Technologies(RGUKT) -Andhra Pradesh (Ongole campus), Andhra pradesh-523001, India
T. Chelladurai
Department of Electronics and Communication and Engineering, PSNA College of Engineering and Technology, Dindigul 624622.Tamil Nadu,India

Published 2024-06-11

Keywords

  • Internet of Things,
  • Diabetes prediction healthcare system,
  • CDC-LSTM,
  • Feature selection,
  • FBDT

How to Cite

Kavitha, T., Amirthayogam, G., Hephzipah, J. J., Suganthi, R., Kumar G, V. A., & Chelladurai, T. (2024). Healthcare Analysis Based on Diabetes Prediction Using a Cuckoo-Based Deep Convolutional Long-Term Memory Algorithm. Babylonian Journal of Artificial Intelligence, 2024, 64–72. https://doi.org/10.58496/BJAI/2024/009

Abstract

In recent years, the demand for mobile medical applications utilizing the Internet of Things (IoT) for diabetes diagnosis has been progressively increasing. Diabetes is commonly known as a chronic illness that presents a significant danger to individuals, analysing when blood sugar levels surpass the typical range. If diabetes is not promptly treated, hyperosmolar conditions can lead to serious health issues like hyperglycaemia and possibly even death.  Since early detection enables lifestyle changes that prevent the disease's progression, it is crucial for diabetes management and health systems. However, diabetes diagnosis has a long computational time and low prediction accuracy. To address this issue, we propose a Cuckoo-Based Deep Convolutional Long-Term Memory (CDC-LSTM) algorithm that increases accuracy by classifying diabetics or non-diabetics. Additionally, we utilize the Standardized Feature Scaler (SFS) method to normalize the variance data by removing the mean of each feature. Moreover, we select the optimal features in the diabetes dataset utilising the Filter-Based Decision Tree (FBDT) technique. Finally, the proposed CDC-LSTM method can be used to distinguish between diabetics and non-diabetics, improving the accuracy of identifying diabetic patients. Additionally, the proposed method can predict diabetes using performance assessments such as precision, recall, and F-measure. Furthermore, the method's accuracy can be improved to 95.18% compared to previous approaches.

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