## 6 Conclusions and Future Work  
This study presented a novel expert system (FNDSS) designed to enhance the management of focal segmental glomerulosclerosis by integrating advanced ML techniques. The system utilized a modular structure that incorporated diagnostic workflows, personalized risk stratification, treatment recommendations, and continuous outcome monitoring. The integration of ML algorithms within the system significantly improved the accuracy and consistency of clinical decision-making, automating complex aspects of the diagnostic and therapeutic process, which traditionally rely on human expertise and can be prone to inconsistencies. The ML models employed in the system, particularly ensemble methods such as LightGBM and random forest, demonstrated superior performance in classifying FSGS outcomes with high precision and recall. These models efficiently handled large, complex datasets, providing valuable insights for clinicians by leveraging data-driven, real-time predictions. The system's ability to continuously learn from new data ensured that its diagnostic and treatment recommendations remained adaptive, thereby facilitating personalized care for patients. The key findings from the evaluation phase highlighted that the expert system effectively reduced diagnostic errors, streamlined treatment protocols, and improved patient outcomes. By automating the classification of disease progression and response to treatment, the system not only accelerated the decision-making process but also mitigated the risk of human error. This enhanced both the efficiency and effectiveness of clinical workflows, offering a robust tool for nephrologists managing FSGS. Despite these advancements, several challenges remain. The system's reliance on standardized, high-quality data underscores the necessity for continuous data curation and validation. Furthermore, the predefined thresholds used for classification must be periodically updated to align with the latest clinical research and evolving biomarkers. The system's performance is also contingent on the availability and accuracy of clinical data inputs, which may vary across healthcare settings. Future work will focus on the following key areas:  
* **Dataset expansion and heterogeneity:** To further enhance the generalizability of the system, it is crucial to integrate larger and more diverse datasets, including multicenter clinical data. This will ensure that the system can adapt to the wide variability found in real-world patient populations and medical practices. * **AI-driven real-time decision support:** A more integrated approach with electronic health records (EHRs) is essential to enable real-time data analysis and predictive decision support. This integration will facilitate the seamless flow of clinical information and enhance the system's responsiveness to dynamic patient conditions. * **Advanced predictive modeling with deep learning:** the current ensemble learning models demonstrated promising results; however, further exploration into deep learning techniques, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), could improve the system's ability to detect subtle patterns and make predictions for more complex cases of FSGS, especially in rare or atypical forms of the disease. * **Automated model training and updating:** Implementing continuous learning protocols will allow the system to autonomously update its models based on new patient data, enhancing its adaptability and predictive capabilities. This will further optimize treatment strategies and improve the system's long-term accuracy. * **Ethical and regulatory compliance:** Given the potential of AI and ML to impact clinical decision-making, it is imperative to ensure that the system adheres to ethical guidelines and regulatory standards, particularly concerning data privacy, transparency, and bias mitigation. Addressing these aspects will ensure that the system can be deployed in clinical practice without compromising patient safety or care quality.