Characterization of Odor Profiles through the Simplified Genetic Algorithm for Disease Diagnostics

Background and Objective: This study investigates the characterization of body odor signatures for early disease detection, aiming to demonstrate the feasibility of using simulated olfactory profiles within a computational diagnostic framework. The motivation arises from the growing interest in non-invasive diagnostic alternatives based on volatile organic compounds (VOCs) emitted by the human body. Materials and methods: A simulation-based approach was implemented using validated VOC datasets to construct binary odor profiles. These profiles were encoded as binary vectors, with each bit indicating the presence or absence of a specific compound. A simplified binary matching algorithm, excluding mutation and crossover operations, was employed to simulate pattern matching. The Hamming distance was used as the fitness function to quantify the similarity between profiles. Results and Discussion: The results indicate that the simplified binary matching algorithm reliably identified pathological odor profiles, producing high similarity scores with reference signatures. Despite the absence of conventional genetic operators, the method consistently converged to optimal or near-optimal matches. These findings emphasize the potential of binary odor encoding for distinguishing between healthy and pathological states, underscoring the robustness of the simplified computational framework. Conclusion: This work presents a novel and interpretable computational model for olfactory-based disease detection using simulated binary VOC patterns. It supports the development of low-cost, non-invasive diagnostic tools in medical contexts. Future research should explore extending the method by incorporating continuous VOC encoding, integrating evolutionary operators, and validating the results with semi-experimental or clinical data.