Random Forest enhancement using improved Artificial Fish Swarm for the medial knee contact force prediction.

Artificial Intelligence in Medicine
Yean ZhuWei Lu

Abstract

Knee contact force (KCF) is an important factor to evaluate the knee joint function for the patients with knee joint impairment. However, the KCF measurement based on the instrumented prosthetic implants or inverse dynamics analysis is limited due to the invasive, expensive price and time consumption. In this work, we propose a KCF prediction method by integrating the Artificial Fish Swarm and the Random Forest algorithm. First, we train a Random Forest to learn the nonlinear relation between gait parameters (input) and contact pressures (output) based on a dataset of three patients instrumented with knee replacement. Then, we use the improved artificial fish group algorithm to optimize the main parameters of the Random Forest based KCF prediction model. The extensive experiments verify that our method can predict the medial knee contact force both before and after the intervention of gait patterns, and the performance outperforms the classical multi-body dynamics analysis and artificial neural network model.

Citations

Dec 10, 2020·Sensors·Georgios GiarmatzisKonstantinos Moustakas
Jan 9, 2021·Environmental Science and Pollution Research International·Feng Gao, Xueyan Shao
Feb 26, 2021·Computers in Biology and Medicine·Yean ZhuWensheng Hou
Apr 16, 2021·Computational Intelligence and Neuroscience·Mohammad SavargivMohammad Reza Keyvanpour

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