An intelligent healthcare framework for hepatocellular carcinoma diagnosis based on aggregated learners from biomedical data utilising explainable artificial intelligence

TL;DR

This paper introduces a new AI model for early diagnosis of liver cancer using biomedical data, which improves accuracy and provides explainable results.

Contribution

The novel contribution is the HCDAL-XAI model, combining multiple AI techniques with explainable AI to enhance HCC diagnosis accuracy and interpretability.

Findings

The HCDAL-XAI model achieved 98.18% accuracy in HCC diagnosis, outperforming existing models.

The model uses an ensemble of SAE, GRU, and DBN for classification and SHAP for explainability.

Abstract

In recent days, biomedical data mining and machine learning (ML) technologies have transformed the healthcare sector, which utilises cutting-edge medical innovative tools to develop effective decision support systems for disease diagnosis and health informatics. Liver cancer (LC) is a major contributor to the global cancer problem. Incidence rates of this disease have improved in several countries in the past decades. As the main histological kind of LC, hepatocellular carcinoma (HCC) constitutes the large majority of LC diagnoses and deaths. HCC is one of the primary reasons for cancer occurrence and fatality. Initial diagnosis of HCC remains the main aim in improving the poor diagnosis of this type of LC. Recognising HCC at an initial stage is frequently related to improved treatment possibilities for patients with small and symptomless tumours. Several artificial intelligence (AI) methods are considered advanced methods for processing and handling composite multimodal data ranging from repetitive clinical variables to higher-resolution medical images. This paper presents a Hepatocellular Carcinoma Diagnosis based on an Aggregated Learners Utilising Explainable Artificial Intelligence (HCDAL-XAI) model from biomedical data. The primary purpose of the HCDAL-XAI model is to deliver an accurate detection model for initial diagnosis and efficient treatment of HCC using progressive methods. Initially, the data pre-processing step uses min-max normalisation. Furthermore, the HCDAL-XAI model employs an ensemble of a sparse autoencoder (SAE), gated recurrent unit (GRU), and deep belief network (DBN) for the classification process. Lastly, the explainable AI (XAI) model employs SHapley Additive exPlanations (SHAP) to enhance the reliability of AI methods by making their decision-making processes understandable to humans. The comparison analysis of the HCDAL-XAI methodology portrayed a greater accuracy value of 98.18% over existing models under the HCC dataset.

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