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• W4286447582 startingPage "108224" @default.
• W4286447582 abstract "Due to the COVID-19 epidemic and the curfew caused by it, many people have sought to find an ADPS on the internet in the last few years. This hints to a new age of medical treatment, all the more so if the number of internet users continues to expand. As a result, automatic illness prediction online applications have attracted the interest of a large number of researchers worldwide. This work aims to develop and implement an automated illness prediction system based on speech. The system will be designed to forecast the sort of ailment a patient is suffering from based on his voice, but this was not feasible during the trial, therefore the diseases were divided into three categories (painful, light pain and psychological pain), and then the diagnose process were implemented accordingly. The medical dataset named speech, transcription, and intent served as the baseline for this study. The smoothness, MFCC, and SCV properties were used in this work, which demonstrated their high representation to human being medical situations. The noise reduction forward-backward filter was used to eliminate noise from wave files captured online in order to account for the high level of noise seen in the deployed dataset. For this study, a hybrid feature selection method was created and built that combined the output of a genetic algorithm (GA) with the inputs of a NN algorithm. Classification was performed using SVM, neural network, and GMM. The greatest results obtained were 94.55% illness classification accuracy in terms of SVM. The results showed that diagnosing illness through speech is a difficult process, especially when diagnosing each type of illness separately, but when grouping the different illness types into groups, depending on the amount of pain and the psychological situation of the patient, the results were much higher." @default.
• W4286447582 created "2022-07-22" @default.
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• W4286447582 date "2022-09-01" @default.
• W4286447582 modified "2023-10-18" @default.
• W4286447582 title "Automatic illness prediction system through speech" @default.
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• W4286447582 doi "https://doi.org/10.1016/j.compeleceng.2022.108224" @default.
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