Differentiation of Gait Using Principal Component Analysis and Application for Parkinson's Disease Monitoring

2018 ◽  
Author(s):  
Donatas Luksys ◽  
Dalius Jatuzis ◽  
Ruta Kaladyte-Lokominiene ◽  
Ramune Buneviciute ◽  
Aleksander Sawicki ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Principal Component Analysis ◽  
Parkinson's Disease ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Monitoring

scholarly journals Application of Fuzzy K-Nearest Neighbor (FKNN) to Detect the Parkinson’s Disease

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
L.N. Desinaini ◽  
Azizatul Mualimah ◽  
Dian C. R. Novitasari ◽  
Moh. Hafiyusholeh
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Principal Component Analysis ◽  
Parkinson's Disease ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Positive Data

AbstractParkinson’s disease is a neurological disorder in which there is a gradual loss of brain cells that make and store dopamine. Researchers estimate that four to six million people worldwide, are living with Parkinson’s. The average age of patients is 60 years old, but some are diagnosed at age 40 or even younger and the worst thing is some patients are late to find out that they have Parkinson's disease. In this paper, we present a diagnosis system based on Fuzzy K-Nearest Neighbor (FKNN) to detect Parkinson’s disease. We use Parkinson’s disease dataset taken from UCI Machine Learning Repository. The first step is normalize the Parkinson’s disease dataset and analyze using Principal Component Analysis (PCA). The result shows that there are four new factors that influence Parkinson’s disease with total variance is 85.719%. In classification step, we use several percentage of training data to classify (detect) the Parkinson's disease i.e. 50%, 60%, 70%, 75%, 80% and 90%. We also use k = 3, 5, 7, and 9. The classification result shows that the highest accuracy obtained for the percentage of training data is 90% and k = 5, where 19 are correctly classified i.e. 14 positive data and 5 negative data, while 1 positive data is classified incorrectly.Keywords: Parkinson's disease; Fuzzy K-Nearest Neighbor; Principal Component Analysis. AbstrakPenyakit Parkinson merupakan kelainan sel saraf pada otak yang menyebabkan hilangnya dopamin pada otak. Para peneliti mengestimasi bahwa, empat sampai enam juta orang di dunia, menderita Parkinson. Penyakit ini rata-rata diderita oleh pasien berusia 60 tahun, namun beberapa orang terdeteksi saat berusia 40 tahun atau lebih muda dan hal terburuk adalah seseorang terlambat untuk mendeteksinya. Di dalam artikel ini, kami menyajikan sistem diagnosa penyakit Parkinson menggunakan metode Fuzzy K-Nearest Neighbor (FKNN). Kami menggunakan Data uji yang diperoleh dari UCI Machine Learning Repository yang telah banyak diterapkan pada masalah klasifikasi. Tahapan pertama yang kami lakukan adalah menormalisasi data kemudian menganalisisnya menggunakan Analisis Komponen Utama (Principal Component Analysis). Hasil Analisis Komponen Utama menunjukkan bahwa terdapat empat factor baru yang mempengaruhi penyakit Parkinson dengan variansi total 87,719%. Pada tahap klasifikasi, kami menggunakan beberapa prosentase data latih untuk mendeteksi penyakit yaitu 50%, 60%, 70%, 75%, 80% and 90%. Selain itu, kami menggunakan beberapa nilai k yaitu 3, 5, 7, and 9. Hasil menunjukkan bahwa klasifikasi dengan akurasi tertinggi diperoleh untuk 90% data latih dengan k = 5, dimana 19 diklasifikasikan secara tepat yaitu 14 data positif dan 5 data negatif, sedangkan satu data positif tidak diklasifikasikan dengan tepat.Keywords: penyakit Parkinson; Fuzzy K-Nearest Neighbor; Analisis Komponen Utama.

Principal Component Analysis of gait in Parkinson's disease: Relevance of gait velocity

2014 ◽  
Vol 39 (3) ◽  
pp. 882-887 ◽  
Author(s):  
Ulrich Dillmann ◽  
Claudia Holzhoffer ◽  
Yvonne Johann ◽  
Sabrina Bechtel ◽  
Stefan Gräber ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Principal Component Analysis ◽  
Parkinson's Disease ◽  
Principal Component ◽  
Component Analysis ◽  
Gait Velocity ◽  
Disease Relevance
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