Automatic illness prediction system through speech

Husam Ali Abdulmohsin; Belal Al-Khateeb; Samer Sami Hasan; Rinky Dwivedi

doi:10.1016/j.compeleceng.2022.108224

Automatic illness prediction system through speech

Comput Electr Eng. 2022 Sep:102:108224. doi: 10.1016/j.compeleceng.2022.108224. Epub 2022 Jul 21.

Authors

Husam Ali Abdulmohsin¹, Belal Al-Khateeb², Samer Sami Hasan¹, Rinky Dwivedi³

Affiliations

¹ Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq.
² College of Computer Science and information Technology, University of Anbar, Anbar, Iraq.
³ Maharaja Surajmal Institute of Technology, New Delhi, India.

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.

Keywords: ADPS, Automated Disease Prediction System; Automatic disease prediction; CPU, Central Processing Unit; Forward-backward filter; GA, Genetic Algorithm; GB, Giga Byte; GMM, Gaussian Mixture Model; MFCC, Mel Frequency Cepstral Co-efficient; Medical speech transcription and intent dataset; Mel frequency Cepstral coefficient; NN, Neural Network; Neural network; RAM, Random Access Memory; RSM, Response Service Methodology; SCV, Spectral Centroid Variability; SVM, Support Vector Machine; Spectral centroid variability.