An LSTM-Assisted Markov Model for Predicting Cancer Stage Transitions: A Simulation Study

Traditional Markov chain models are commonly used to represent cancer progression but are limited by the memoryless assumption and reduced ability to capture nonlinear temporal dependencies. This study proposes a hybrid LSTM-assisted Markov chain framework that integrates sequential deep learning wi...

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Bibliografski detalji

Glavni autori:	Abdullahi Anas, Aliyu Usman Kinafa, Aliyu Umar Shelleng
Format:	Artigo
Jezik:	Inglês
Izdano:	Umaru Musa Yar'adua University, Katsina, Nigeria 2026-03-01
Serija:	UMYU Scientifica Journal
Teme:	Cancer progression prediction Markov Chain Long Short-Term Memory (LSTM) LSTM-Assisted Markov mode Comparative model performance
Online pristup:	https://publications.umyu.edu.ng/scientifica/index.php/usci/article/view/534
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