A NEW APPROACH TO IDENTIFY THE FFI SLEEP DISORDER USING INVASIVE WEED OPTIMIZATION TECHNIQUE

Abstract

Fatal Familial Insomnia (FFI) is a severe sleep ailment. FFI is one of the sleep disorder which you have trouble falling and/or staying asleep. The condition can be short-term (acute) or can last a long time (chronic). The detection of FFI at an early stage can provide better mitigation from severe health impairments. An accurate method to detect FFI can improve the quality of life significantly. Sleep disorder classification can facilitate this process.  In this paper, data pre-processing stage, the datasets were analyzed and normalized using feature extraction and selection mechanisms. Feature reduction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA) are done and next optimal feature selection using Invasive weed Optimization algorithm is performed. Machine Learning (ML) technique i.e Support Vector Machine (SVM) is used in our experiment.