QML-FFSD: A Novel Approach for Early Detection of SCDs through Feature Fusion of Antibiotics Composition and Symptoms Data using Quantum ML

Spinal cord disorders (SCDs) are a significant cause of morbidity worldwide, and early detection of SCDs is crucial for improving patient outcomes. In the existing clinical systems, automated techniques are needed to identify patterns and correlations, which can help in the curated diagnosis and timely treatment of SCD. In this study, we propose a novel approach for the early detection of SCDs through feature fusion of harmful pain-relieving antibiotic composition and disease symptoms using quantum ML called Quantum ML-Feature Fusion of Antibiotic Composition and Symptoms Data (QML-FFSD). The proposed approach involves the use of quantum ML algorithms to identify relevant features, demonstrating the effectiveness of the proposed approach in detecting SCDs at an early stage. The QML-FFSD approach achieved high accuracy and specificity in identifying patients with SCDs, indicating its potential as a valuable tool for clinical decision-making’s representation of the patient’s condition. The resulting representation is then used to train a quantum classifier to predict the likelihood of a SCD. The proposed work shows 0.86 precision, 0.85 recall, and 0.86 F1 score for detecting abnormal spinal cord conditions as compared to normal spinal cord conditions of 0.74 precision, 0.76 recall, and 0.75 F1-score, respectively. The overall accuracy of the model is 0.82, indicating improved performance in classifying spinal cord conditions. The proposed work improves the accuracy of diagnosis and reduces the likelihood of misdiagnosis, which can lead to further complications.