The indoor environment is widely acknowledged as a non-pharmacological tool for regulating residents’ mental health. In dormitory environments with relatively high residential density, the mental health of university students requires particular attention. This study surveyed 445 students from a northern Chinese university and used structural equation modeling (SEM) to analyze the impact of perceived indoor environmental quality (IEQ)—including thermal, lighting, acoustics, indoor air quality, and overcrowding—on self-reported anxiety symptoms. The results indicated the following: (1) students’ perceptions of dormitory IEQ significantly affected anxiety symptoms, explaining 40% of the variance; (2) anxiety symptoms associated with the IEQ were mainly characterized by anxiety and panic (r = 0.91, p < 0.001); (3) subjective perceptions of the acoustic environment (r = −0.55, p < 0.001) and indoor air quality (r = −0.15, p < 0.05) were key predictors of anxiety, while thermal environment, lighting environment, and overcrowding were not significant. The findings enrich the IEQ system and provide directions for optimizing the dormitory indoor environment from the perspective of student mental health, with implications for other types of residential buildings.

The relationship between coronary heart disease (CHD) and complex urban built environments remains a subject of considerable uncertainty. The development of predictive models via machine learning to explore the underlying mechanisms of this association, as well as the formulation of intervention policies and planning strategies, has emerged as a pivotal area of research. A cross-sectional dataset of hospital admissions for CHD over the course of a year from a hospital in Dalian City, China, was assembled and matched with multi-source built environment data via residential addresses. This study evaluates five machine learning models, including decision tree (DT), random forest (RF), eXtreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and support vector machine (SVM), and compares them with multiple linear regression models. The results show that DT, RF, and XGBoost exhibit superior predictive capabilities, with all R² values exceeding 0.70. The DT model performed the best, with an R² value of 0.818, and the best performance was based on metrics such as MAE and MSE. Additionally, using explainable AI techniques, this study reveals the contribution of different built environment factors to CHD and identifies the significant factors influencing CHD in cold regions, ranked as age, Digital Elevation Model (DEM), house price (HP), sky view factor (SVF), and interaction factors. Stratified analyses by age and gender show variations in the influencing factors for different groups: for those under 60 years old, Road Density is the most influential factor; for the 61–70 age group, house price is the top factor; for the 71–80 age group, age is the most significant factor; for those over 81 years old, building height is the leading factor; in males, GDP is the most influential factor; and in females, age is the most influential factor. This study explores the feasibility and performance of machine learning in predicting CHD risk in the built environment of cold regions and provides a comprehensive methodology and workflow for predicting cardiovascular disease risk based on refined neighborhood-level built environment factors, offering scientific support for the construction of sustainable healthy cities.