Quantum-Inspired Deep Feature Selection and Transfer Learning Approach for Leukemia Disease Classification

Jyoti Gautam; Sachin Harne

doi:10.59367/jd053p69

Authors

Jyoti Gautam Department of CSE, RSR- RCET, Bhilai, Chhattisgarh, India
Sachin Harne Department of CSE, RSR- RCET, Bhilai, Chhattisgarh, India

DOI:

https://doi.org/10.59367/jd053p69

Keywords:

Leukemia, Quantum computing, Machine learning, deep learning, classification

Abstract

The intricacy and variability of leukemia make it extremely difficult to diagnose and classify. In this work, we provide a novel method for improved leukemia subtype classification that combines classical machine learning methods with quantum-inspired deep learning. Our Quantum-Inspired Deep Learning model is evaluated against Random Forest, Deep Neural Network, and Support Vector Machine (SVM) models using two benchmark datasets: the Leukemia and Microarray Quality Control (MAQC) datasets. On the Leukemia dataset, our model yields an accuracy of 0.92, precision of 0.93, recall of 0.91, and F1 score of 0.92. These results are impressive with an MCC of 0.84, an AUC-ROC of 0.95, and sensitivity and specificity of 0.91 and 0.93, respectively. Comparably, the model achieves 0.93 accuracy, 0.94 precision, 0.92 recall, and 0.93 F1 score on the MAQC dataset. In addition, it shows 0.92 and 0.94 sensitivity and specificity, respectively, with 0.96 AUC-ROC and 0.87 MCC. The outcomes highlight the superiority of our Quantum-Inspired Deep Learning model in precisely identifying leukemia subtypes, with potentially positive consequences for tailored treatment plans and prognostication forecasting in medical environments.

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