Parkinnet is a groundbreaking AI architecture designed to detect early-stage Parkinson's disease through speech analysis. This project leverages advanced machine learning techniques to identify subtle changes in speech patterns, which are often among the first signs of Parkinson's disease. Parkinnet was developed with the goal of creating an accessible, non-invasive diagnostic tool that could be used by healthcare professionals and individuals for early detection and intervention.

Key Features:

AI-Driven Speech Analysis: Parkinnet analyzes specific speech characteristics, focusing on subtle changes in voice frequency, tremors, and rhythm that are typically undetectable to the human ear but are indicative of Parkinson's disease. The model uses audio clips of patients pronouncing the vowel 'A,' enabling it to detect even minor variations that suggest early onset of the disease.
High Precision and Recall: The model achieved 100% recall and precision in its tests, meaning it correctly identified all positive cases without any false negatives or positives. This high accuracy makes Parkinnet a powerful tool in the early diagnosis of Parkinson's, which is crucial for timely treatment and potentially slowing the progression of the disease.
Mobile and Telehealth Integration: One of the key goals of Parkinnet was to make it widely accessible. To that end, the model was optimized for mobile deployment and can be integrated into telehealth platforms. This allows individuals in underprivileged and rural communities, who might otherwise have limited access to neurological specialists, to receive early diagnosis without the need for expensive, invasive tests.
Global Impact and Accessibility: Parkinnet is designed to reach populations worldwide, especially those who may not have easy access to traditional healthcare resources. By focusing on mobile integration and ease of use, the platform brings advanced Parkinson's detection to remote areas, significantly improving healthcare accessibility.

Parkinnet is a testament to how AI and machine learning can transform the healthcare landscape. By providing a scalable and accessible tool for early Parkinson's detection, it not only increases the chances of early intervention but also democratizes healthcare, making advanced diagnostics available to underserved populations globally.