A new machine learning model to predict the prognosis of cardiogenic brain infarction.

ABSTRACT

Cardiogenic cerebral infarction (CCI) is a disease in which the blood supply to the blood vessels in the brain is insufficient due to atherosclerosis or stenosis of the coronary arteries in the patient's heart, which leads to neurological deficits. To predict the pathogenic factors of cardiogenic cerebral infarction, this paper proposes a machine learning based analytical prediction model. 494 patients with CCI who were hospitalized for the first time were consecutively included in the study between January 2017 and December 2021, and followed up every three months for one year after hospital discharge. Clinical, laboratory and imaging data were collected, and predictors associated with relapse and death in CCI patients at six months and one year after discharge were analyzed using univariate and multivariate logistic regression methods, meanwhile established a new machine learning model based on the enhanced moth-flame optimization (FTSAMFO) and the fuzzy K-nearest neighbor (FKNN), called BITSAMFO-FKNN, which is practiced on the dataset related to patients with CCI. Specifically, this paper proposes the spatial transformation strategy to increase the exploitation capability of moth-flame optimization (MFO) and combines it with the tree seed algorithm (TSA) to increase the search capability of MFO. In the benchmark function experiments FTSAMFO beat 5 classical algorithms and 5 recent variants. In the feature selection experiment, ten times ten-fold cross-validation trials showed that the BITSAMFO-FKNN model proved actual medical importance and efficacy, with an accuracy value of 96.61%, sensitivity value of 0.8947, MCC value of 0.9231, and F-Measure of 0.9444. The results of the trial showed that hemorrhagic conversion and lower LVDD/LVSD were independent risk factors for recurrence and death in patients with CCI. The established BITSAMFO-FKNN method is helpful for CCI prognosis and deserves further clinical validation.