Neural Hypernetwork Approach for Pulmonary Embolism diagnosis

TL;DR

This paper presents a Neural Hypernetwork approach combining Q-analysis and machine learning to improve pulmonary embolism diagnosis, reducing unnecessary CT scans with high accuracy.

Contribution

It introduces a novel Neural Hypernetwork method that integrates topological data analysis with machine learning for medical diagnosis.

Findings

Achieved 94% accuracy in PE diagnosis

Outperformed previous statistical and topological methods

Demonstrated the effectiveness of Q-analysis in data proximity

Abstract

This work introduces an integrative approach based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The objective of the application of neural hyper-network to pulmonary embolism (PE) is to improve diagnose for reducing the number of CT-angiography needed. Hypernetworks, based on topological simplicial complex, generalize the concept of two-relation to many-body relation. Furthermore, Hypernetworks provide a significant generalization of network theory, enabling the integration of relational structure, logic and analytic dynamics. Another important results is that Q-analysis stays close to the data, while other approaches manipulate data, projecting them into metric spaces or applying some filtering functions to highlight the intrinsic relations. A pulmonary embolism (PE) is a blockage of the main artery of the lung or one of its branches, frequently fatal. Our study uses data on 28 diagnostic features of 1,427 people considered to be at risk of PE. The resulting neural hypernetwork correctly recognized 94% of those developing a PE. This is better than previous results that have been obtained with other methods (statistical selection of features, partial least squares regression, topological data analysis in a metric space).