A machine learning approach for Premature Coronary Artery Disease Diagnosis according to Different Ethnicities in Iran

Premature coronary artery disease (PCAD) refers to the early onset of the disease, usually before the age of 55 for men and 65 for women. Coronary Artery Disease (CAD) develops when coronary arteries, the major blood vessels supplying the heart with blood, oxygen, and nutrients, become clogged or diseased. This is often due to many risk factors, including lifestyle and cardiometabolic ones, but few studies were done on ethnicity as one of these risk factors, especially in PCAD. In this study, we tested the rank of ethnicity among the major risk factors of PCAD, including age, gender, body mass index (BMI), visceral obesity presented as waist circumference (WC), diabetes mellitus (DM), high blood pressure (HBP), high low-density lipoprotein cholesterol (LDL-C), and smoking in a large national sample of patients with PCAD from different ethnicities. All patients who met the age criteria underwent coronary angiography to confirm CAD diagnosis. The weight of ethnicity was compared to the other eight features using feature weighting algorithms in PCAD diagnosis. In addition, we conducted an experiment where we ran predictive models (classification algorithms) to predict PCAD. We compared the performance of these models under two conditions: we trained the classification algorithms, including or excluding ethnicity. This study analyzed various factors to determine their predictive power influencing PCAD prediction. Among these factors, gender and age were the most significant predictors, with ethnicity being the third most important. The results also showed that if ethnicity is used as one of the input risk factors for classification algorithms, it can improve their efficiency. Our results show that ethnicity ranks as an influential factor in predicting PCAD. Therefore, it needs to be addressed in the PCAD diagnostic and preventive measures.

Adaptive formation motion planning and control of autonomous underwater vehicles using deep reinforcement learning

Creating safe paths in unknown and uncertain environments is a challenging aspect of leader-follower formation control. In this architecture, the leader moves toward the target by taking optimal actions, and followers should also avoid obstacles while maintaining their desired formation shape. Most of the studies in this field have inspected formation control and obstacle avoidance separately. The present study proposes a new approach based on deep reinforcement learning (DRL) for end-to-end motion planning and control of under-actuated autonomous underwater vehicles (AUVs). The aim is to design optimal adaptive distributed controllers based on actor-critic structure for AUVs formation motion planning. This is accomplished by controlling the speed and heading of AUVs. In obstacle avoidance, two approaches have been deployed. In the first approach, the goal is to design control policies for the leader and followers such that each learns its own collision-free path. Moreover, the followers adhere to an overall formation maintenance policy. In the second approach, the leader solely learns the control policy, and safely leads the whole group towards the target. Here, the control policy of the followers is to maintain the predetermined distance and angle. In the presence of ocean currents, communication delays, and sensing errors, the robustness of the proposed method under realistically perturbed circumstances is shown. The efficiency of the algorithms has been evaluated and approved using a number of computer-based simulations.