Parkinson’s progression - let's put the data to use
Parkinson’s disease progression has long puzzled the medical community. With an influx of data, papers and researchers our team decided to undertake the task of getting closer to discovering the nature of this disease by analysing biomarker data indicative of the disease's advancement.
Goal - recognise Parkinson's before it strikes
The challenge lies in identifying novel biomarkers contributing to Parkinson’s progression. It’s believed that variations in protein and peptide levels are crucial indicators of the disease’s development. The aim is to predict future MDS-UPDRS (Unified Parkinson’s Disease Rating Scale) scores for patients, providing a roadmap for devising effective, personalised treatments, given the absence of a Parkinson’s cure.
Solution - Machine Learning model that predicts disease
Despite existing monitoring methods, predicting Parkinson’s progression with certainty remains elusive.
Our specialist proposed a Machine Learning (ML) model capable of predicting individualised MDS-UPDRS scores based on patients’ protein and peptide levels.
To make it happen we’ve analysed data from over 10,000 subjects, including patients’ peptides/proteins levels (taken from Cerebrospinal Fluid samples) and past UPDRS scores.
Results - accurate prediction aiding diagnosis
A functional ML model was developed, capable of predicting Parkinson’s progression. Starting with raw data, the team imputed missing records, trained algorithms, and successfully made accurate predictions about Parkinson’s progression, providing a valuable tool for diagnosis and treatment improvement.
This endeavour was not only an important learning experience but also an application of data analysis and ML knowledge to a real-world challenge, making a positive impact on individuals with Parkinson’s disease.
For a deeper understanding and collaboration on similar projects, please get in touch