## 2 Literature Review
### Predictive Modeling for Neurodegenerative Diseases
To fully realize the benefits of these technologies in proactive healthcare, however, issues like data quality (related to inconsistent or incomplete data and differences in data formats and collection methods across sources), model interpretability (for complex models that healthcare providers must comprehend and trust), ethical considerations, and clinical validation and approval of predictive models must be addressed. Predictive models can identify patterns that may be imperceptible to human caregivers/medical professionals and that may be indicative of disease onset and progression. Despite their potential, before clinical validation, predictive models are still being deployed and used at small scales, and they are not integrated into standardized protocols for diagnosis and disease tracking. For the moment, predictive models are deployed locally to aid and assist healthcare professionals, but the human professional is still responsible for making responsible decisions. However, as different models undergo clinical validation and refinement, their integration into the broader healthcare system is expected to grow, leading to more widespread and standardized use. Example clinical trials for predictive models built on actual patient data are numerous and have various applications in healthcare, such as predicting the risk of coronary heart disease based on a specific health questionnaire using fuzzy logic and logistic regression [35], prediction tools for patients and physicians for different types of cancer risk assessment and outcome predictions [36], predicting the risk of hospital readmission [37], predicting outbreaks of diseases--an application that was used in real life during the COVID-19 pandemic, AI-assisted dermoscopy integrated into smart devices [38], and even AI-assisted surgery integrated into Da Vinci robots [39]. **Predictive modeling for neurodegenerative diseases** is a rapidly evolving field with the potential to significantly improve patient outcomes through early detection and personalized interventions. Predictive modeling for AD, PD, and MS involves the use of advanced computational techniques to improve early diagnosis, monitor disease progression, and tailor personalized treatment strategies. These models integrate various types of data, including genetic, clinical, and imaging data, to identify patterns and biomarkers indicative of disease onset and progression. In the case of AD, predictive models may be used for early diagnosis and for analyzing disease progression. AI models may use genetic data comprising information about the APOE gene, associated with an increased risk of developing AD [2], and imaging data from magnetic resonance imaging (MRI) or positron emission tomography (PET) scans to detect brain changes or deposits of certain types of cells that are characteristic of AD [40]. In the case of predictive modeling to be integrated into a health monitoring system at home, predictive modeling can be used for cognitive evaluation, sleep quality assessment, activity monitoring, or analyzing speech to detect changes in language usage or speech production. In the case of PD, predictive models serve critical roles in early detection and disease progression analysis. These AI-driven models utilize diverse data sources, such as genetic information, including mutations in genes associated with PD risk, and imaging data from technologies like MRI or dopamine transporter (DAT) scans, to identify structural and functional changes in the brain related to PD symptoms [40; 41]. In the area of home health monitoring systems, predictive modeling enables continuous assessment of motor symptoms such as abnormal movements, posture, and balance. It can also aid in evaluating cognitive function, tracking physical activities, and analyzing speech patterns to detect changes indicative of PD progression or medication response. In the case of MS, predictive modeling may be of use in monitoring disease progression and improving patient management.