Genetics-Based Machine Learning for The Assessment of Certain Neuromuscular Disorders  

Oleh:

Pattichis, C. S. ; Schizas, C. N.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 2 (1996), page 427-439.
Topik: LEARNING; genetics - based machine; learning; neuromuscular

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Clinical electromyography (EMG) provides useful information for the diagnosis of neuromuscular disorders. The utility of artificial neural networks (ANN's) in classifying EMG data trained with backpropagation or Rohonen's self - organizing feature maps algorithm has recently been demonstrated. The objective of this study is to investigate how genetics - based machine learning (GBML) can be applied for diagnosing certain neuromuscular disorders based on EMG data. The effect of GBML control parameters on diagnostic performance is also examined. A hybrid diagnostic system is introduced that combines both neural network and GBML models. Such a hybrid system provides the end - user with a robust and reliable system, as its diagnostic performance relies on more than one learning principle. GBML models demonstrated similar performance to neural - network models, but with less computation. The diagnostic performance of neural network and GBML models is enhanced by the hybrid system.

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