Empowering Glioma Prognosis With Transparent Machine Learning and Interpretative Insights Using Explainable AI

Abstract

The primary objective of this research is to create a reliable technique to determine whether a patient has glioma, a specific kind of brain tumour, by examining various diagnostic markers, using a variety of machine learning as well as deep learning approaches, and involving XAI (explainable artificial intelligence) methods. Through the integration of patient data, including medical records, genetic profiles, algorithms using machine learning have the ability to predict how each individual will react to different medical interventions. To guarantee regulatory compliance and inspire confidence in AI-driven healthcare solutions, XAI is incorporated. Machine learning methods employed in this study includes Random Forest, decision trees, logistic regression, KNN, Adaboost, SVM, Catboost, LGBM classifier, and Xgboost whereas the deep learning methods include ANN and CNN. Four alternative XAI strategies, including SHAP, Eli5, LIME, and QLattice algorithm, are employed to comprehend the predictions of the model. The Xgboost, a ML model achieved accuracy, precision, recall, f1 score, and AUC of 88%, 82%, 94%, 88%, and 92%, respectively. The best characteristics according to XAI techniques are IDH1, Age at diagnosis, PIK3CA, ATRX, PTEN, CIC, EGFR and TP53. By applying data analytic techniques, the objective is to provide healthcare professionals with practical tool that enhances their capacity for decision-making, enhances resource management, and ultimately raises the bar for patient care. Medical experts can customise treatments and improve patient outcomes by taking into account patient’s particular characteristics. XAI provides justifications to foster faith amongst patients and medical professionals who must rely on AI-assisted diagnosis and treatment recommendations.

Keywords

• Glioma
• molecular makeup
• explainable artificial intelligence (XAI)
• SHAP
• LIME
• QLattice