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Machine Learning - Based Prediction of Genotoxicity in Peripheral Erythrocytes of Fish (Labeo catla): A Comparative Analysis of Algorithms

Year: 2024

Type: article

Abstract: This study utilizes machine learning ML algorithms to predict the accuracy of a genotoxicity dataset, focusing on nuclear abnormalities in peripheral erythrocytes of Labeo catla. Eight ML algorithms, including Logistic Regression, K - nearest neighbour, Lazy. KStar, DecisionStump, Hoeffding Tree, RandomForest, and RandomTree, were tested using the WEKA tool. Among these, RandomForest demonstrated the highest predictive accuracy with an area under the ROC curve of 91%. These results indicate that ML algorithms, particularly RandomForest, provide an effective approach for predicting genotoxicity in fish species.

Source: International Journal of Science and Research (IJSR)

DOI: 10.21275/sr24922214440

Authors: Kousik Seal, Soumendra Nath Talapatra

Institution: Seacom Skills University

Language: English

Topic: Carcinogens and Genotoxicity Assessment

Subfield: Cancer Research

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4402870385