NSOTree: Neural Survival Oblique Tree

TL;DR

NSOTree combines neural networks and oblique decision trees to improve survival analysis by enhancing interpretability and performance, addressing the limitations of existing methods.

Contribution

The paper introduces NSOTree, a novel neural survival oblique tree that integrates neural network capabilities with tree interpretability for survival analysis.

Findings

NSOTree outperforms traditional methods on simulated datasets.

NSOTree achieves comparable or better results on real datasets.

The model offers improved interpretability without sacrificing accuracy.

Abstract

Survival analysis is a statistical method employed to scrutinize the duration until a specific event of interest transpires, known as time-to-event information characterized by censorship. Recently, deep learning-based methods have dominated this field due to their representational capacity and state-of-the-art performance. However, the black-box nature of the deep neural network hinders its interpretability, which is desired in real-world survival applications but has been largely neglected by previous works. In contrast, conventional tree-based methods are advantageous with respect to interpretability, while consistently grappling with an inability to approximate the global optima due to greedy expansion. In this paper, we leverage the strengths of both neural networks and tree-based methods, capitalizing on their ability to approximate intricate functions while maintaining interpretability. To this end, we propose a Neural Survival Oblique Tree (NSOTree) for survival analysis. Specifically, the NSOTree was derived from the ReLU network and can be easily incorporated into existing survival models in a plug-and-play fashion. Evaluations on both simulated and real survival datasets demonstrated the effectiveness of the proposed method in terms of performance and interpretability.