首页|Trakya University Reports Findings in Artificial Intelligence (Cognitive activit y analysis of Parkinson’s patients using artificial intelligence techniques)
Trakya University Reports Findings in Artificial Intelligence (Cognitive activit y analysis of Parkinson’s patients using artificial intelligence techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Edirne, Turkey, b y NewsRx journalists, research stated, “The development of modern Artificial Int elligence (AI) based models for the early diagnosis of Parkinson’s disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models.” The news correspondents obtained a quote from the research from Trakya Universit y, “Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-supp ort approach that doctors can benefit from. A dataset was created by asking 40 P D and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying dr awings of individuals using Support Vector Machine (SVM), Random Forest (RF), an d Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out ( LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitt ing scenarios. To further improve the performances of classifiers, Principal Com ponent Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly. The highest accuracy among mach ine learning based classifiers was obtained as 90% with SVM classi fier using non-template drawings with PCA application. The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimiz es environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals.”
EdirneTurkeyEurasiaArtificial Inte lligenceEmerging TechnologiesMachine Learning
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