Recently, speech pattern analysis applications in building predictive tele diagnosis and tele monitoring models for diagnosing\nParkinsonââ?¬â?¢s disease (PD) have attracted many researchers. For this purpose, several data sets of voice samples exist; the UCI data set\nnamed ââ?¬Å?Parkinson Speech Data set with Multiple Types of Sound Recordingsââ?¬Â has a variety of vocal tests, which include sustained\nvowels, words, numbers, and short sentences compiled from a set of speaking exercises for healthy and people with Parkinsonââ?¬â?¢s\ndisease (PWP). Some researchers claim that summarizing the multiple recordings of each subject with the central tendency and\ndispersion metrics is an efficient strategy in building a predictive model for PD. However, they have overlooked the point that a PD\npatient may show more difficulty in pronouncing certain terms than the other terms.Thus, summarizing the vocal tests may lead\ninto loss of valuable information. In order to address this issue, the classification setting must take what has been said into account.\nAs a solution, we introduced a new framework that applies an independent classifier for each vocal test.The final classification result\nwould be a majority vote from all of the classifiers. When our methodology comes with filter-based feature selection, it enhances\nclassification accuracy up to 15%.
Loading....