Treffer: An MLP-Based Deep Neural Network Incorporating SMOTE-Tomek Approach for Robust Prediction of Liver Disorders.

Liver disorders are among the most common diseases worldwide, and their timely diagnosis and prediction can significantly improve treatment outcomes. In recent years, the application of artificial intelligence, particularly machine learning and deep learning algorithms, has gained tremendous importance in the medical field, leading to reduced healthcare costs. This study utilized the ILPD dataset from the UCI Machine Learning Repository, which comprises 583 liver patient records with 11 features. A predictive framework based on a Multilayer Perceptron (MLP) was employed to predict liver disorders. To address class imbalance in the binary classification dataset, the Synthetic Minority Oversampling Technique (SMOTE)- Tomek approach was implemented to improve data balance. Robust scaling was applied to manage the presence of outlier values. Finally, the proposed method's performance was compared with three wellknown machine learning algorithms. Five-fold cross-validation was employed across all classifiers to enhance evaluation robustness. All simulations were conducted using Python. The results indicate that the proposed method achieves superior performance, with an accuracy of 90.90%, surpassing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

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