Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke

• Published in: BMC neurology 2024-05, Vol.24 (1), p.156-156, Article 156
• Main Authors: Abujaber, Ahmad A, Imam, Yahia, Albalkhi, Ibrahem, Yaseen, Said, Nashwan, Abdulqadir J, Akhtar, Naveed
• Format: Article
• Language: English
• Subjects: Adult; Aged; Artificial intelligence; Ataxia; Blood pressure; Blood sugar; Body mass index; Body temperature; Classification; Decision making; Decision support; Diabetes; Diagnosis; Early Diagnosis; Expatriates; Female; Health aspects; Humans; Ischemia; Learning algorithms; Machine Learning; Male; Medical research; Medicine, Experimental; Middle Aged; Obesity; Patients; Population; Posterior circulation syndrome (PCS); Posterior stroke diagnosis; Registries; Risk factors; Stroke; Stroke (Disease); Stroke - diagnosis; Stroke - physiopathology; Stroke risk factors; Tomography; Type 2 diabetes; Variables; Veins & arteries
• ISSN: 1471-2377; 1471-2377
• DOI: 10.1186/s12883-024-03638-8

Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management. We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew's correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance. The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value. This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient's clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.