Hybrid Soft Computing and Explainable AI for Analyzing Medical Data and Clinical Recommendations

The study will develop an advanced model that will integrate soft computing and explainable artificial intelligence (XAI) methods to analyze biomedical data and make accurate clinical suggestions. The proposed system will address the issue of lack of transparency of the traditional deep learning algorithms with a hybrid and multi-stage methodology of breast tumor diagnosis. The method is based on three main pillars: First, a genetic algorithm (GA) is utilized as an intelligent mechanism for dimensionality reduction and selecting the most influential medical features, thus improving the model's efficiency. Second, the extracted features are analyzed by artificial neural networks (ANNs) and determine the probability of malignancy with a classification accuracy of over 97%. Third, to address the variability and uncertainty in medical diagnosis, a fuzzy logic-based inference system is created that will combine the probability of the neural network with actual clinical measurements (tumor area) to determine the risk index. The greatest scientific impact of such a study is the incorporation of an XAI decision module. This system is not only digital output, yet generates textual clinical recommendations as well as clear medical justifications on each one of the patients.