Explainable Machine Learning for Atrial Fibrillation in the General Population Using a Generalized Additive Model　― A Cross-Sectional Study ―

Abstract

AF have an approximately 5-fold higher risk of stroke than those without AF. 7 Therefore, the development of AF should be predicted at the early onset.Artificial intelligence (AI) is gaining attention in preventive medicine, and there is an urgent need for the detection and prediction of AF. AI methods can automatically identify obscure but significant patterns from individual clinical data to predict the clinical outcomes of a patient. Atrial fibrillation (AF) is the most common arrhythmia, with a global prevalence estimated at high as 46.3 million, with annual increases of 3.8 million cases in recent years. 1-4 AF is associated with an increased risk of thromboembolic stroke and heart failure, which is not only life threatening, but can also result in severe sequelae, leaving patients with severe mobility impairments or requiring nursing care. 5,6 Individuals with

Prediction and causal inference of cardiovascular and cerebrovascular diseases based on lifestyle questionnaires

“…Medical institutions in charge of health checkups were sent identical manuals in accordance with the guidelines of the respective associations and checkups were conducted accordingly. During the checkups, clinicians performed a standard consultation and recorded data on height, weight, waist circumference, blood pressure, biochemical test results, urinalysis, and lifestyle questionnaires 6 . The study was approved by the Ethics Committee of the Kanazawa Medical Association (KMA) (No.…”

“…The KMA database contains information on various clinical parameters as previously reported 6 . Six variables were collected from the dataset: age, sex, body mass index (BMI), waist circumference, systolic blood pressure (SBP), and diastolic blood pressure (DBP).…”

“…In recent years, artificial intelligence (AI) has attracted significant attention because of its use in preventive medicine. AI methods can automatically identify important patterns in an individual’s clinical data and predict disease onset and prognosis 6 , 7 . Several studies have demonstrated the high accuracy of AI models for stroke and coronary artery disease 8 , 9 .…”