2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 1 , PP: 361-373, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
S. Puvaneswari 1 * , G. Indırani 2
Doi: https://doi.org/10.54216/JISIoT.180128
Genetic disorder is an outcome of transformation in deoxyribonucleic acid (DNA) system, which is progressed or natural from blood relation. Such transformations might lead to deadly illnesses like Alzheimer’s, cancer, and much more. The disorder of single gene kind is affected by a change in a solitary gene in DNA. The chromosomal disorder kind is affected when a genetic material or a portion of chromosome is removed or substituted in the structure of DNA. Complex illnesses are caused by the alteration in over one gene exhibit in the DNA. In recent times, the usage of artificial intelligence (AI)-based deep learning (DL) systems has exposed excellent achievement in the prognosis and prediction of diverse illnesses. The latent of DL models are employed to forecast genetic disorder at an initial phase utilizing the genome data for appropriate treatment. This paper presents a Deep Feature Selection Framework for Genetic Disorder Detection Using Convolutional Autoencoder and Metaheuristic Optimization (DFSFGDD-CAEMO) model. The aim of DFSFGDD-CAEMO model is to develop an accurate DNA-based genetic disorder classification model using advanced techniques for early and reliable disease diagnosis. Initially, the min-max normalization method is employed in the data pre-processing stage for converting an input data into a beneficial format. Besides, the Aquila optimizer (AO) method has been deployed for the selection of feature process in order to select the most significant features from a dataset. For the classification procedure, the proposed DFSFGDD-CAEMO technique designs Convolutional Autoencoder (CAE) method. At last, the hyperactive parameter tuning process is performed through enhanced pelican optimization algorithm (EPOA) for improving the classification performance of CAE model. The experimental evaluation of the DFSFGDD-CAEMO technique occurs using benchmark dataset. The experimentation results indicated out the enhanced performance of the DFSFGDD-CAEMO system when equated to existing approaches.
Genetic Disorder Detection , Convolutional Autoencoder , Enhanced Pelican Optimization Algorithm , Feature Selection , Deoxyribonucleic Acid
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