International Journal of Clinical Case Reports, 2025, Vol.15, No.5, 209-218 http://medscipublisher.com/index.php/ijccr 211 predictive effects. Some automated processing systems, such as tools based on AutoML, are increasingly being applied to simplify data collection and integration processes, including data cleaning, annotation, and encoding, in order to transform raw and diverse data into usable information. Multi-source data often leads to problems such as inconsistent data quality, many missing values, and inconsistent detailed data content. To solve these problems, complex data processing steps and corresponding professional knowledge are often required to ensure that the comprehensive data is both reliable and truly reflects the actual situation. With more advanced visualization tools, such as parallel coordinate graphs, the characteristics of the data can be understood more clearly, which is helpful for formulating integration strategies and thereby provides support for more stable feature extraction and model construction (Zhao et al., 2024). 3.2 Data preprocessing: missing values, outliers and normalization processing Data preprocessing is a crucial step in preparing data, as the original data is usually incomplete and may contain outliers or have inconsistent formats. Common processing methods include standardization, identification of outliers and normalization. These operations can enhance data quality and make the model more stable and reliable. Some automated tools can also reduce manual operations and lower the probability of errors. For instance, missing values can be filled in by using k-nearest neighbor impution or machine learning methods. Normalization can adjust different features to similar scales, thereby helping the model converge faster and improving interpretability. Although automation methods are becoming increasingly mature, the pretreatment process is still not simple and usually requires the repeated adjustment of parameters with the help of professional experience. Different preprocessing methods will directly affect the model's performance. If missing values or outliers are not handled properly, errors may be introduced or the prediction accuracy may be affected (Chicco et al., 2022). Therefore, the design and inspection of the preprocessing scheme need to be carried out with extreme caution, especially in health informatics applications where data quality requirements are extremely high. 3.3 Feature engineering: feature selection, dimension reduction and construction Feature engineering is of great significance for extracting useful information from complex data and enhancing the effectiveness of chronic disease prediction models. Through feature selection methods, such as SHAP-based importance assessment, filtering method, encapsulation method and embedding method, the most relevant variables can be screened out, redundancy can be reduced and understandability can be enhanced (Zhang, 2024). Dimensionality reduction techniques such as principal component analysis (PCA) and autoencoders can further simplify data and alleviate the computational pressure brought by high-dimensional data (Santoso and Priyadi, 2025). In addition to selection and compression, feature construction-through manual design, ensemble learning or deep learning-can also generate new information features and better capture latent patterns (Verdonck et al., 2021). Some automated feature engineering tools, such as tsfresh for time series, can automatically extract and screen statistically significant features, accelerating analysis and application implementation (Christ et al., 2018). Combining these methods can help establish more stable and scalable models in big data-driven health prediction (Rong et al., 2019; Mumuni and Mumuni, 2024). 4 Construction Methods of Chronic Disease Prediction Models 4.1 Statistical models Logistic regression and Cox regression have always been commonly used methods for predicting chronic diseases due to their ease of use and clear results. Logistic regression is highly effective in binary classification problems, such as using clinical indicators to predict whether diabetes, cardiovascular diseases or chronic kidney diseases will occur. Studies have found that when the data is not highly nonlinear or ultra-high-dimensional, the performance of logistic regression can be similar to that of some complex machine learning methods. Cox regression is commonly used in survival analysis and can estimate the occurrence time of events, so it is of great value for predicting disease progression and patient risk (Figure 1) (Zhang et al., 2023).
RkJQdWJsaXNoZXIy MjQ4ODYzNA==