Polynomial-SHAP analysis of liver disease markers for capturing of complex feature interactions in machine learning models.

Liver disease diagnosis is pivotal for effective patient management, and machine learning techniques have shown promise in this domain. In this study, we investigate the impact of Polynomial-SHapley Additive exPlanations analysis on enhancing the performance and interpretability of machine learning models for liver disease classification. Our results demonstrate significant improvements in accuracy, precision, recall, F1_score, and Matthews correlation coefficient across various algorithms when polynomial- SHapley Additive exPlanations analysis is applied.

MACCoM: A multiple attention and convolutional cross-mixer framework for detailed 2D biomedical image segmentation.

The UNet architecture, which is widely used for biomedical image segmentation, has limitations like blurred feature maps and over- or under-segmented regions. To overcome these limitations, we propose a novel network architecture called MACCoM (Multiple Attention and Convolutional Cross-Mixer) - an end-to-end depthwise encoder-decoder fully convolutional network designed for binary and multi-class biomedical image segmentation built upon deeperUNet. We proposed a multi-scope attention module (MSAM) that allows the model to attend to diverse scale features, preserving fine details and high-level semantic information thus useful at the encoder-decoder connection.

Comparative performance analysis of Boruta, SHAP, and Borutashap for disease diagnosis: A study with multiple machine learning algorithms.

Interpretable machine learning models are instrumental in disease diagnosis and clinical decision-making, shedding light on relevant features. Notably, Boruta, SHAP (SHapley Additive exPlanations), and BorutaShap were employed for feature selection, each contributing to the identification of crucial features. These selected features were then utilized to train six machine learning algorithms, including LR, SVM, ETC, AdaBoost, RF, and LR, using diverse medical datasets obtained from public sources after rigorous preprocessing.

Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation.

Recent research indicates that early detection of breast cancer (BC) is critical in achieving favorable treatment outcomes and reducing the mortality rate associated with it. With the difficulty in obtaining a balanced dataset that is primarily sourced for the diagnosis of the disease, many researchers have relied on data augmentation techniques, thereby having varying datasets with varying quality and results. The dataset we focused on in this study is crafted from SHapley Additive exPlanations (SHAP)-augmentation and random augmentation (RA) approaches to dealing with imbalanced data.

Structure and Computational Studies of New Sulfonamide Compound: {(4-nitrophenyl)sulfonyl}tryptophan.

Synthesis of sulfonamide through an indirect method that avoids contamination of the product with no need for purification has been carried out using the indirect process. Here, we report the synthesis of a novel sulfonamide compound, ({4-nitrophenyl}sulfonyl)tryptophan (DNSPA) from 4-nitrobenzenesulphonylchloride and L-tryptophan precursors. The slow evaporation method was used to form single crystals of the named compound from methanolic solution.