## 2 Literature Review
### Predictive Modeling for Neurodegenerative Diseases  
Predictive modeling is an area of statistics and ML based on data analytics techniques that aims to model a system in order to predict certain states of interest that the system may get to. When applied in healthcare, predictive modeling may be used for multiple purposes: develop early diagnosis models that allow early intervention and potentially slow disease progression; identify risk factors; personalize treatment plans and screening programs; predict disease evolution and outbreak; and optimize patient care in general. Most of the ML techniques may be applied for healthcare predictions, starting from simple logistic regression techniques to random forests, support vector machines, complex neural networks with multiple hidden layers and different activation functions, and complex combined architectures. Depending on the required task, different models have been proven to be more effective than others. Recent advancements in ML algorithms applied in other fields may lead to improvements in applications for healthcare. For example, advancements in the area of computer vision may be applied to medical image processing for disease diagnosis based on retinal images, CT or MRI data, or creating digital twins for different simulations [26; 27; 28]; advancements in natural language processing (NLP) may be applied in healthcare for different predictive analytics for electronic health records, for sentiment analysis, or even medical diagnosis [29; 30]. Different computational improvements for time series forecasting may come from different fields and find applications in healthcare. Some specific examples are: the techniques presented in [31] for analyzing motion patterns of animals in the wild based on non-Brownian stochastic processes may be applied in analyzing and predicting motion-related symptoms of people with neurodegenerative diseases; pattern recognition in financial fraud detection presented in [32] may be applied in identifying fraud and unusual patterns in healthcare systems; predictive maintenance in industrial equipment presented in [33] may be applied for predictive health monitoring; and environmental monitoring using satellite data presented in [34] may be applied for epidemiology. When building predictive models, the quality of the data used is of the essence and directly affects the performance of the built models. There are two major types of ML algorithms: supervised algorithms that learn from labeled data and unsupervised learning algorithms that make decision rules based on unlabeled data by finding patterns, structures, and relationships in the learning data. In both cases, the quality and quantity of data provided for learning are fundamental to the performance of the model. For predictive models for healthcare, data must be obtained in clinically validated conditions, rigorously checked and curated, and, often, obtained during long periods of time by periodically or continuously monitoring the same subjects. High-quality healthcare data must include comprehensive and accurate clinical records, demographic information, and relevant biomarkers, all collected following standardized protocols to ensure consistency and reproducibility. This meticulous approach to data collection and management is essential for developing robust predictive models that can support early diagnosis, personalized treatment plans, and improved patient outcomes. Modern trends demonstrate advances in multimodal data integration (combining data from medical records, wearables, genetic analysis, and lifestyle factors to create comprehensive models), real-time monitoring (using IoMT devices to gather real-time data on physical and cognitive parameters, analyzing them in real-time, and providing instant alerts and recommendations based on the obtained results), and ML techniques (deep learning and combining multiple models to improve predictive performance and robustness), which enhance the accuracy and relevance of predictive models.