Features selection for building an early diagnosis machine learning model for Parkinson's disease
| Autor: | Mohamed Fares, Abu Bakr Soliman, Mohamed M. Elhefnawi, Mahmoud Al-Hefnawy |
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| Rok vydání: | 2016 |
| Předmět: |
Parkinson's disease
Scale (ratio) Computer science business.industry 0206 medical engineering Crossover Feature selection 02 engineering and technology Machine learning computer.software_genre medicine.disease 020601 biomedical engineering Support vector machine Statistical classification Rating scale 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer Selection (genetic algorithm) |
| Zdroj: | 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR). |
| DOI: | 10.1109/icaipr.2016.7585225 |
| Popis: | In this work, different approaches were evaluated to optimize building machine learning classification models for the early diagnosis of the Parkinson disease. The goal was to sort the medical measurements and select the most relevant parameters to build a faster and more accurate model using feature selection techniques. Decreasing the number of features to build a model could lead to more efficient machine learning algorithm and help doctors to focus on what are the most important measurements to take into account. For feature selection we compared the Filter and Wrapper techniques. Then we selected a good machine learning algorithm to detect which technique could help us by calculate the crossover scores for each technique. This research is based on a dataset which was created by Athanasius Tsanas and Max Little of the University of Oxford, in collaboration with 10 medical centers in the US and Intel Corporation. This target of these medical measurements is to find the Unified Parkinson's disease rating scale (UPDRS) which is the most commonly used scale for clinical studies of Parkinson's disease. |
| Databáze: | OpenAIRE |
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