Disease Prediction: The Role of Explainable Machine Learning for Lungs Cancer

Abstract

Lung cancer remains the leading cause of cancer-related deaths worldwide, highlighting the urgent need for improved diagnostic and prognostic tools. The identification and prognosis of lung cancer may be improved by recent developments in machine learning. The lack of interpretability in classic machine learning models, however, prevents them from being widely used in clinical settings. Explainable AI (XAI) tools, which provide insights into the decision-making processes of models, overcome this problem. This paper explores the application of explainable machine learning approaches to improve the detection and prediction of lung cancer. Our results demonstrate how explainable models can provide physicians with valuable information while increasing diagnosis accuracy. A potent tool for enhancing the identification and prognosis of lung cancer is machine learning. The analysis of histopathological images for precise cancer classification has shown significant promise thanks to recent developments in deep learning algorithms. Convolutional neural networks (CNNs) have been shown in studies to have the ability to identify between benign and cancerous lung tissue with high accuracy. For example, a research that used CNNs to diagnose lung cancer on digital pathology images had accuracy rates of over 97%. Traditional machine learning models, however, frequently lack interpretability, which makes it challenging for physicians to comprehend the logic behind the predictions despite their remarkable success. This restriction has made it more difficult for AI to be widely used in healthcare contexts. Researchers have resorted to explainable AI (XAI) methods in order to tackle this problem. XAI techniques seek to increase the transparency of AI models by offering insights into their decision-making processes.