## 5 Conclusions
When monitoring patients with PD, AD, or MS, the ability to identify emotions is extremely important because it provides early warning of disease progression and has a significant influence on general well-being. Emotional changes, such as lack of interest, anxiety, or depression, may appear early in these disorders, affecting the patient's commitment to medical care and negatively impacting quality of life. The emotion detection algorithm is a deep learning model based on a sequential CNN-LSTM architecture. In order to create a model that generalizes well on new data and is not specific to data from one database, recorded in certain conditions, four databases have been selected and used for training and testing of the model. For the same purpose of increasing the generalizability of the built model, and avoiding overfitting, several augmentation techniques have been performed on the input data. These techniques help the model perform robustly on new data acquired in real-world scenarios. This approach advances the field by addressing challenges in generalization and variability and demonstrating the integration of affective computing into a complex practical application. The built model has an overall accuracy of 82% across the three classes. The minimum precision value is achieved for the neutral class, the least represented class in the training data. Positive emotions are predicted with 83.85% accuracy and 79.56% recall, while negative emotions are predicted with 84.41% accuracy and 86.33% recall. _A direct comparison of the results of the model integrated into the NeuroPredict platform with those of other authors is hard to perform_, as results are strictly dependent on the datasets used, and current work uses a combination of datasets that improves model generalization and performances in the wild but makes it difficult to compare performances to other work. The average accuracy of 82% allows the algorithm to be implemented in a real world scenario, considering the fact that the integration of the algorithm includes an average of the results for a longer speech segment that allows for the correction of false interpretations. In order to compare the results with results from other work, different models were built on the separate datasets used in the literature. Results show similar performances in terms of average accuracy, with better performances for the Tess and Savee datasets and lower performances on data from Ravdess and Crema-D. This comparison shows that the approach used for building the SER algorithm integrated into the NeuroPredict platform, similar to the approach used for building separate SER algorithms with smaller datasets, is valid and leads to good results, similar to those found in recent works. Analyzing the limitations of the presented algorithm, two drawbacks may be identified: (a) Concerning the reliance on multiple databases for training the emotion detection algorithm, while enhancing generalizability, it may still not capture the full diversity of real-world conditions, potentially limiting its effectiveness in entirely new environments. Additionally, the voice-based emotion detection algorithm, although robust, may face challenges in noisy or poor-quality audio conditions, which are common in everyday settings; (b) although an overall accuracy of 82% considering the amount of various data is a good result, this still means that approximately 18% of data are incorrectly predicted. Future efforts will focus on enhancing both the accuracy of the prediction algorithm and refining the labeling of longer speech segments by determining the predominant emotional state detected in the data. Additionally, future work will involve expanding the diversity of training datasets, improving noise robustness in voice analysis. Privacy concerns related to voice recordings are mitigated by the platform's approach of not storing any vocal signals; instead, only the final classification results--indicating the assigned emotional state--are retained.