International Journal of Clinical Case Reports, 2025, Vol.15, No.5, 209-218 http://medscipublisher.com/index.php/ijccr 215 When choosing a model, a balance needs to be struck among accuracy, interpretability and computational cost. Case-based reasoning and integration methods have advantages in individualized prediction and adaptation to different patient groups, but usually have higher requirements for the technical environment and professional support (Nenova and Shang, 2021; Al-Jamimi, 2024). The selection of models should be based on clinical goals, resource conditions and actual application scenarios. 6.2 Challenges of big data quality and privacy protection Common problems with medical big data include: numerous missing values, inconsistent formats, and difficulty in merging data from different channels. If not handled through appropriate preprocessing, feature selection and strict validation, these problems will reduce the accuracy and generalization ability of the model (Al-jamimi, 2024). In chronic disease data, there is often an imbalance among categories, which can easily lead to a high rate of false negatives. Therefore, more advanced methods are needed to handle skew distribution while ensuring a match with clinical needs (Tsai et al., 2025). Privacy protection is also particularly important because predictive models require sensitive information such as electronic medical records. Only on the premise of ensuring data security, complying with relevant regulations, and maintaining patient trust can big data be used more safely in medical scenarios. Therefore, researchers are experimenting with methods such as data anonymization processing, secure sharing mechanisms, and federated learning, hoping to reduce the risk of privacy leakage while maintaining the modeling effect (Alam et al., 2024). Properly handling these challenges is an important prerequisite for promoting the rational use of big data in the field of chronic disease prediction. 6.3 The need for clinical acceptability and interpretability The acceptance of predictive models in clinical practice depends on their interpretability and whether they can provide doctors with clear and actionable references. Although the prediction accuracy of machine learning and deep learning models is high, "black box" features may reduce the trust and willingness of clinical personnel to use them (Delpino et al., 2022). Therefore, recent studies have proposed explainable AI methods that combine attention mechanisms, feature importance analysis, etc., to increase model transparency and assist medical decision-making (Rashid et al., 2022; Kim et al., 2023). In addition, the practical application also requires strict verification, an easy-to-use operation interface, and a good connection with the existing clinical process to ensure that the model assists rather than interferes with diagnosis and treatment (Battineni et al., 2020; Tsai et al., 2025). This also requires data scientists, doctors and policymakers to continue to collaborate, properly handle ethical issues and promote the wide adoption of models. Ultimately, achieving a balance among prediction accuracy, interpretability and ease of use is the key to enabling big data-driven chronic disease prediction models to maximize their value in clinical practice. 7 Concluding Remarks Different chronic disease prediction models have their own characteristics: statistical models are often adopted because they are easy to understand and use, machine learning models can better discover complex patterns in big data, and deep learning models have more advantages when dealing with high-dimensional and multimodal data. There is no single method that is optimal in all circumstances. On the contrary, their complementary characteristics indicate that by combining different methods, more stable and clinically demand-oriented predictions can be obtained, especially in the context of the continuous increase in the scale and complexity of medical data. The future direction of improvement might be hybrid modeling, which combines statistics, machine learning and deep learning to fully leverage their respective advantages. Meanwhile, the integration of different types of data, such as the combination of clinical records, medical images and wearable device information, is expected to further enhance the predictive effect and support more personalized risk assessment. These methods help to make up for the deficiencies of a single model and reflect the complex characteristics of chronic diseases more comprehensively.
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