Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension

• Published in: Computers in biology and medicine 2023-10, Vol.165, p.107408-107408, Article 107408
• Main Authors: Yu, Xiaoming, Qin, Wenxiang, Lin, Xiao, Shan, Zhuohan, Huang, Liyao, Shao, Qike, Wang, Liangxing, Chen, Mayun
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Arteries; Blood platelets; Blood pressure; Cardiac catheterization; Clinical medicine; Complications; Disease diagnosis; Feature selection; FKNN; Heart; Hypertension; Intubation; Medical diagnosis; Medical imaging; Methods; Optimization; Optimization algorithms; Physicians; Pulmonary arteries; Pulmonary artery; Pulmonary hypertension; Random follower search; Right ventricular failure; RIME; Search methods; Triangular gaming search; Veins & arteries; Ventricle; Warning systems
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107408

Pulmonary hypertension (PH) is an uncommon yet severe condition characterized by sustained elevation of blood pressure in the pulmonary arteries. The delaying treatment can result in disease progression, right ventricular failure, increased risk of complications, and even death. Early recognition and timely treatment are crucial in halting PH progression, improving cardiac function, and reducing complications. Within this study, we present a highly promising hybrid model, known as bERIME_FKNN, which constitutes a feature selection approach integrating the enhanced rime algorithm (ERIME) and fuzzy K-nearest neighbor (FKNN) technique. The ERIME introduces the triangular game search strategy, which augments the algorithm's capacity for global exploration by judiciously electing distinct search agents across the exploratory domain. This approach fosters both competitive rivalry and collaborative synergy among these agents. Moreover, an random follower search strategy is incorporated to bestow a novel trajectory upon the principal search agent, thereby enriching the spectrum of search directions. Initially, ERIME is meticulously compared to 11 state-of-the-art algorithms using the IEEE CEC2017 benchmark functions across diverse dimensionalities such as 10, 30, 50, and 100, ultimately validating its exceptional optimization capability within the model. Subsequently, employing the color moment and grayscale co-occurrence matrix methodologies, a total of 118 features are extracted from 63 PH patients' and 60 healthy individuals' images, alongside an analysis of 14,514 recordings obtained from these patients utilizing the developed bERIME_FKNN model. The outcomes manifest that the bERIME_FKNN model exhibits a conspicuous prowess in the realm of PH classification, attaining an accuracy and specificity exceeding 99%. This implies that the model serves as a valuable computer-aided tool, delivering an advanced warning system for diagnosis and prognosis evaluation of PH. •The performance of ERIME algorithm is enhanced by Triangular gaming search and Random follower search.•Compared with other high-performance optimizers, ERIME obtains higher quality optimal solutions in IEEE CEC 2017 functions.•The bERIME for pulmonary hypertension is proposed using FKNN.•bERIME_FKNN has technical advantages in the analysis of pulmonary hypertension.•bERIME_FKNN can be used as a tool to assist in the diagnosis of pulmonary hypertension.