Betti-Number Based Machine-Learning Classifier Frame-work for Predicting the Hepatic Decompensation in Patients with Primary Sclerosing Cholangitis
- Resource Type
- Conference
- Authors
- Singh, Yashbir; Jons, William; Sobek, Joseph D; Eaton, John E.; Erickson, Bradley J.; Anderies, Barrett J.; Jagtap, Jaidip
- Source
- 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) Computing and Communication Workshop and Conference (CCWC), 2022 IEEE 12th Annual. :0159-0162 Jan, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Manifolds
Biomedical equipment
Conferences
Training data
Machine learning
Medical services
Topology
Hepatic decompensation
Betti number
Topological data analysis
Magnetic resonance
- Language
This paper proposes a computationally efficient method for estimating the topology of manifold data in the context of medical applications. Betti numbers computed with persistent homology tools can be more useful for hepatic decompensation prediction in patients with Primary Sclerosing Cholangitis. We propose an alternative method that uses Betti numbers to estimate hepatic decompensation status. The results show that the proposed methodology is capable of distinguishing between hepatic decompensation and non-hepatic decompensation status. We discovered that using a Betti number-based machine-learning approach, we can make accurate predictions from small datasets, such as predicting who is likely to have hepatic decompensation and those who do not.