Hybrid Random Feature Selection and Recurrent Neural Network for Diabetes Prediction

Hybrid Random Feature Selection and Recurrent Neural Network for Diabetes Prediction

This paper proposes a novel two-stage ensemble framework combining Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) with randomized feature selection to enhance diabetes prediction accuracy and calibration. The method first trains multiple LSTM/BiLSTM base models on dynamically sampled...

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Bibliographic Details
Main Authors: Oyebayo Ridwan Olaniran, Aliu Omotayo Sikiru, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Nada MohammedSaeed Alharbi
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Mathematics
Subjects:
recurrent neural network
long short-term memory
diabetes prediction
ensemble learning
Online Access:https://www.mdpi.com/2227-7390/13/4/628
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