Performance Comparison Between Deep Neural Network and Machine Learning Based Classifiers for Huntington Disease Prediction From Human DNA Sequence

• Published in: IEEE Transactions on Computational Biology and Bioinformatics Vol. 22; no. 1; pp. 52 - 63
• Main Authors: Vishnuppriya, C., Tamilpavai, G.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2025
• Subjects: Accuracy; Algorithms; CAG repeat; chaos game representation; Classification algorithms; Classification tree analysis; Computational Biology - methods; Convolutional neural networks; Deep Learning; deep neural network; deoxyribo nucleic acid; Diseases; DNA; DNA - genetics; Feature extraction; Humans; huntington disease; Huntington Disease - diagnosis; Huntington Disease - genetics; Long short term memory; Machine Learning; Neural Networks, Computer; Proteins; Sequence Analysis, DNA - methods; Support vector machines
• ISSN: 2998-4165; 1557-9964; 2998-4165; 1557-9964
• DOI: 10.1109/TCBB.2024.3493203

Huntington Disease (HD) is a type of neurodegenerative disorder which causes problems like psychiatric disturbances, movement problem, weight loss and problem in sleep. It needs to be addressed in earlier stage of human life. Nowadays Deep Learning (DL) based system could help physicians provide second opinion in treating patient's disease. In this work, human Deoxyribo Nucleic Acid (DNA) sequence is analyzed using Deep Neural Network (DNN) algorithm to predict the HD disease. The main objective of this work is to identify whether the human DNA is affected by HD or not. Human DNA sequences are collected from National Center for Biotechnology Information (NCBI) and synthetic human DNA data are also constructed for process. Then numerical conversion of human DNA sequence data is done by Chaos Game Representation (CGR) method. After that, numerical values of DNA data are used for feature extraction. Mean, median, standard deviation, entropy, contrast, correlation, energy and homogeneity are extracted. Additionally, the following features such as counts of adenine, thymine, guanine and cytosine are extracted from the DNA sequence data itself. The extracted features are used as input to the DNN classifier and other machine learning based classifiers such as NN (Neural Network), Support Vector Machine (SVM), Random Forest (RF) and Classification Tree with Forward Pruning (CT WFP ). Six performance measures are used such as Accuracy, Sensitivity, Specificity, Precision, F1 score and Mathew Correlation Co-efficient (MCC). The study concludes DNN, NN, SVM, RF achieve 100% accuracy and CT WFP achieves accuracy of 87%.