The Adaptive Manifold Concept with Regularized Autoencoders (AMRAE) algorithm is introduced as a novel dimensionality reduction technique that integrates manifold learning with autoencoders to capture the intrinsic geometry of high-dimensional data effectively. The study evaluates the impact of various adjustments and enhancements within the "Manifold Adjustment Box," including different regularization techniques, activation functions, and architectural choices, across diverse datasets. Key findings demonstrate that configurations such as Leaky ReLU Activation and Batch Norm Layer consistently improve accuracy, results highlight the flexibility and robustness of AMRAE. The results underscore... More >

This research presents a diagnostic method for Polycystic Ovary Syndrome (PCOS), a common hormonal disorder among women of childbearing age. The study applied machine learning classifiers—Random Forest, CatBoost, and MLP—to the Kaggle PCOS dataset, enhanced by BorutaShap and SMOTE feature selection methods. The ensemble classifier achieved an F1 score of 93.54% and an accuracy of 96.71%. These results demonstrate AI's potential to improve PCOS diagnosis for broader clinical applications. Future research should integrate genetic and epigenetic factors into AI models, validated through clinical trials. Key contributions include: using an ensemble of machine learning classifiers, advanced f... More >