Explainable artificial intelligence for LDL cholesterol prediction and classification.

INTRODUCTION: Monitoring LDL-C levels is essential in clinical practice because there is a direct relation between low-density lipoprotein cholesterol (LDL-C) levels and atherosclerotic heart disease risk. Therefore, measurement or estimate of LDL-C is critical. The present study aims to evaluate Artificial Intelligence (AI) and Explainable AI (XAI) methodologies in predicting LDL-C levels while emphasizing the interpretability of these predictions. MATERIALS AND METHODS: We retrospectively reviewed data from the Laboratory Information System (LIS) of Ankara Etlik City Hospital (AECH). We included 60.217 patients with standard lipid profiles (total cholesterol [TC], high-density lipoprotein cholesterol, and triglycerides) paired with same-day direct LDL-C results. AI methodologies, such as Gradient Boosting (GB), Random Forests (RF), Support Vector Machines (SVM), and Decision Trees (DT), were used to predict LDL-C and compared directly measured and calculated LDL-C with formulas. XAI techniques such as Shapley additive annotation (SHAP) and locally interpretable model-agnostic explanation (LIME) were used to interpret AI models and improve their explainability. RESULTS: Predicted LDL-C values using AI, especially RF or GB, showed a stronger correlation with direct measurement LDL-C values than calculated LDL-C values with formulas. TC was shown to be the most influential factor in LDL-C prediction using SHAP and LIME. The agreement between the treatment groups based on NCEP ATPIII guidelines according to measured LDL-C and the LDL-C groups obtained with AI was higher than that obtained with formulas. CONCLUSIONS: It can be concluded that AI is not only a reliable method but also an explainable method for LDL-C estimation and classification.

Automatic Diagnosis of Obstructive Sleep Apnea/Hypopnea Events Using Respiratory Signals.

Obstructive sleep apnea is a sleep disorder which may lead to various results. While some studies used real-time systems, there are also numerous studies which focus on diagnosing Obstructive Sleep Apnea via signals obtained by polysomnography from apnea patients who spend the night in sleep laboratory. The mean, frequency and power of signals obtained from patients are frequently used. Obstructive Sleep Apnea of 74 patients were scored in this study. A visual-scoring based algorithm and a morphological filter via Artificial Neural Networks were used in order to diagnose Obstructive Sleep Apnea. After total accuracy of scoring was calculated via both methods, it was compared with visual scoring performed by the doctor. The algorithm used in the diagnosis of obstructive sleep apnea reached an average accuracy of 88.33 %, while Artificial Neural Networks and morphological filter method reached a success of 87.28 %. Scoring success was analyzed after it was grouped based on apnea/hypopnea. It is considered that both methods enable doctors to reduce time and costs in the diagnosis of Obstructive Sleep Apnea as well as ease of use.