DOI: 10.35229/jaes.1989248 ISSN: 2548-0006

Differentiation of Vector Species Using COI-Based Data: A Case Study of Simuliidae (Diptera) with Multi-Class Machine Learning

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DNA sequence identification of selected vector species complexes of Simuliidae (Simulium equinum, Simulium metallicum, Simulium callidum, Simulium damnosum and Simulium ochraceum) was tested via machine learning methods. The cytochrome oxidase I gene region was used for comparisons. Sequence data obtained from the NCBI database were converted to digital format with 2-bit one-hot coding. SVM (Support Vector Machines), LDA (Linear Discriminant Analysis), Logistic Regression, and Random Forest algorithms were applied to classify the data. The 5-fold cross-validation method was used to compare the algorithm performances. The SVM model achieved the highest accuracy as a robust classifier for DNA-based species identification.

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