Firdaus Firdaus, Siti Nurmaini*, Anggun Islami, Annisa Darmawahyuni, Ade Iriani Sapitri, Muhammad Naufal Rachmatullah, Bambang Tutuko, Akhiar Wista Arum, Muhammad Irfan Karim, Yultrien Yultrien, Ramadhana Noor Salassa Wandya
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3419-3441, 2025, DOI:10.32604/cmc.2024.055906
- 17 February 2025
Abstract Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated… More >
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