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Parkinson’s Disease Progression Prediction

A hand shaking while holding a glass of water. The hand is clearly affected by Parkinson's Disease, as the tremors are visible.
The progression of Parkinson's disease remains a mystery. Many doctors, scientists, and researchers are investigating the topic to identify potential biomarkers for the condition. The acceleration of data collection provides us with much more information than ever before. At DAC, our team of experts is also attempting to find new indicators of the disease progression through the analysis of collected data.

Outline of the problem

The objective of the competition was to identify new biomarkers that contribute to the progression of Parkinson’s disease. Experts believe that alterations in protein and peptide levels signal further development of the disorder. By forecasting future MDS-UPDRS scores for patients, we could devise more effective treatments for each unique case. Since there is no known cure for Parkinson’s disease, we must understand all phases of its development in order to ease unpleasant symptoms.

Proposed solution

After carefully analyzing the problem, the team came up with the innovative idea of developing an ML model that could predict MDS-UPDRS scores for each individual patient, personalized to their level of protein and peptide.

The solution required a deep dive into the data to uncover hidden patterns and relationships. These patterns were then used to train machine learning algorithms that could predict the progression of Parkinson’s disease in months interval.

Technology overlay

To improve our understanding of Parkinson’s disease and develop new treatments, we used a variety of technologies, including:

  • Python for scripting and experiment architecture
  • Pandas for tabular data handling and EDA (Exploratory Data Analysis)
  • CatBoost and XGBoost to build decision trees  for classification tasks
  • Optuna for hyperparameter optimization
  • TabNet (DNN) for finding patterns in tabular data

Monitoring and Predicting Parkinson’s Disease


Doctors currently monitor the progression of Parkinson’s disease (PD) by taking a medical history, performing a physical examination, ordering tests, using rating scales, and tracking the person’s progress over time. However, there is no way to predict with certainty how quickly PD will progress in each individual.

Now, researchers among many others are developing a system to help predict the development of PD using machine learning (ML) algorithms and models. This system could be used to identify people who are at risk of developing PD and to provide them with early treatment. Early treatment could help to slow the progression of the disease and improve the quality of life for people with PD.

The development of this system is a promising step towards improving the diagnosis and treatment of PD. By better understanding the factors that contribute to PD progression, researchers can develop more effective treatments and improve the lives of people with this disease.

Initial talks and kickoff

A team of researchers was tasked with analyzing tabular data of over 10,000  subjects, including patients’ peptides/proteins levels (taken from Cerebrospinal Fluid sample) and past Unified Parkinson’s Disease Rating Scale (UPDRS) score with additional clinical state on medication. Data was collected during cyclic visits and often included incomplete information.

The team’s goal was to identify any hidden relationships between the data that could help them better understand the progression of PD. To do this, they had to:

  • Normalize and perform the imputation of missing  data to ensure that all of the subjects had complete records with values on the same scale.
  • Perform statistical operations on the data, such as calculating means, standard deviations, and correlations.
  • Research correlations between different variables to identify any potential relationships.
  • Deep research methods responsible for grading the importance of selected variables and predicting PD progression.
  • Show the progress of the disease with graphs to visualize the changes in PD symptoms over time  and evaluate the computational pipeline on local cross-validation.

Experts’ work was successful in identifying that the relations between given peptides and protein data is not sufficient to accurately forecast the PD progression.. That was due to incompleteness of provided data and incorrect incorporation of control group to the data set. Stronger signal was found in the visits frequency, although it was not explored by DAC experts.

Team composition

The team  was self-organized and efficient. When new tasks came in, the person who was interested in the topic was responsible for that part. This allowed the team to be flexible and adaptable to changing priorities.

While the team was eager to deepen their current skills in data analysis, especially in processing tabular data. They had to master new methods and techniques, and they often had to troubleshoot problems. It was a tough nut to crack, but the team persevered and eventually gained a deep understanding of tabular data processing.

The team’s hard work paid off. DAC engineers were able to successfully process the tabular data and gain valuable insights. This knowledge will be essential for future projects. Team is confident that they can handle any challenge that comes their way.


Our team developed a working machine learning (ML) model that can predict the progression of Parkinson’s disease (PD). We started with raw data and imputed missing records required for accurate predictions… Then, we trained ML algorithms on found relations to make predictions about the PD progression in set time intervals. Our computational pipeline was able to make correct predictions, and we are confident that it can be used to improve the diagnosis and treatment of PD.


This project was a valuable learning experience that allowed us to apply our knowledge of data analysis and machine learning to a real-world problem. We are proud to have made a positive impact on the lives of people with Parkinson’s disease.

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