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XGBoosting Early Detection: Advancing Parkinson's Disease Diagnosis through Machine Learning

Francisco Santos, D.

2023-10-25 health informatics
10.1101/2023.10.23.23297368 medRxiv
Show abstract

Parkinsons disease (PD) is a significant neurodegenerative disorder, affecting millions worldwide. Early and accurate diagnosis is pivotal for effective treatment. In this study, we explore the application of XGBoost, a powerful machine learning algorithm, for classifying PD based on speech signal features. Our research presents a systematic methodology, addressing data preparation, model development, and performance evaluation. The XGBoost model achieved an accuracy of 80.09% in distinguishing individuals with and without PD, demonstrating its promise for disease classification. Moreover, we discuss the potential for XGBoost in healthcare and highlight the need for further research in the intersection of machine learning and disease diagnosis.

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The top 10 journals account for 50% of the predicted probability mass.

1
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Top 0.1%
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Top 0.2%
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50% of probability mass above
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.