Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records

The purpose of this study is to show how ensemble learning-driven machine learning algorithms outperform individual machine learning algorithms at predicting ovarian cancer on a biomarker dataset. Additionally, this study provides model explanations using explainable Artificial Intelligence methods, The method involved gathering and combining 49 risk factors from 349 patients. We hypothesize that ensemble machine learning systems are superior to individual Machine Learning systems in predicting ovarian cancer. The Machine Learning system consists of five individual Machine Learning and five ensemble Machine Learning systems were trained using K-10 cross validation protocols. These training models were then used to predict the development of benign ovarian tumors and ovarian cancer tumors patients. The AUC and Accuracy metrics for ensemble machine learning increased by 19% and 16%. The MCC and Kappa scores for ensemble Machine Learning also increased over individual machine learning by 29% and 33%, respectively. As a result, we draw the conclusion that ensembled-based algorithms outperform individual machine learning in terms of ovarian carcinoma prediction

ABM-OCD: Advancing ovarian cancer diagnosis with attention-based models and 3D CNNs

Ovarian cancer remains a leading cause of cancer-related mortality among women worldwide. Traditional diagnostic methods often lack the precision required for early detection and accurate subtype classification. In this study, we address the challenge of automating ovarian cancer diagnosis by introducing Attention-Based Models (ABMs) in combination with 3D Convolutional Neural Networks (CNNs). Our research seeks to enhance the accuracy and efficiency of ovarian cancer diagnosis, particularly in distinguishing between serous, mucinous, and endometrioid subtypes. Conventional diagnostic approaches are limited by their reliance on manual interpretation of medical images and fail to fully exploit the rich information present in MRI scans. The proposed work leverages ABMs to dynamically focus on critical regions in MRI scans, enabling enhanced feature extraction and improved classification accuracy. We demonstrate our approach on a well-curated dataset, OvaCancerMRI-2023, showcasing the potential for precise and automated diagnosis. Experimental results indicate superior performance in cancer subtype classification compared to traditional methods, with an accuracy of 94% and F1 score of 0.92. Our findings underscore the potential of ABMs and 3D CNNs in revolutionizing ovarian cancer diagnosis, paving the way for early intervention and more effective treatment strategies. In conclusion, this research marks a significant advancement in the realm of ovarian cancer diagnosis, offering a promising avenue for improving patient outcomes and reducing the burden of this devastating disease. The integration of ABMs and 3D CNNs holds substantial potential for enhancing the accuracy and efficiency of ovarian cancer diagnosis, particularly in subtyping, and may contribute to early intervention and improved patient care

A hybrid intelligent model based on logistic regression and fuzzy multiple-attribute decision-making for credit evaluation

. One of the crucial issues in data mining is to select an appropriate classification algorithm. Due to it usually involves many criteria, the duty of algorithm selection can be widely described as multiple-attribute decision-making (MADM) problems, including credit risk evaluation. Many different MADM approaches select classifiers based on different perspectives, and hence they might generate diverse classifiers' rankings. This paper aims to propose a hybrid intelligent model to overcome credit risk assessment problems based on logistic regression and the fuzzy MADM method. Firstly, the Ordinal Priority Approach (OPA) method evaluates attributes involved in credit risk problems by considering professional assessments of a decision-maker and calculates a weight for each criterion. Secondly, all categorical data converted into triangular-fuzzy numbers (TFNs) and numerical data are evaluated using the MADM instrument to obtain an optimal solution dataset and logistic regression to calculate the probabilities of the optimal dataset. In this experimental study, three existing classification techniques and the proposed intelligent model evaluate three banking credit datasets with a different number of criteria under numerical and categorical data types. The prediction accuracy results generated by the proposed model are compared with the three existing classification methods. The results exhibit that there are slight differences between the three datasets. The experimental results demonstrate the proposed intelligent model has superiority in classifying the credit loan recipients especially for categorical datasets