Bio-Inspired Optimization for Medical Data Mining

3Parkinson’s Disease Detection Using Voice and Speech—Systematic Literature Review

Ronak Khatwad¹, Suyash Tiwari¹, Yash Tripathi¹, Ajay Nehra¹ and Ashish Sharma²*

¹Dept. of CSE, IIIT Kota, MNIT Jaipur, Jaipur, India

²Dept. of CSE, Manipal University Jaipur, Jaipur, India

Abstract

Parkinson’s disease (PD) is normally diagnosed in a person after a thorough physical examination by a doctor that considers the patient’s medical history, neurological examination, evaluation of motor symptoms, and other supporting tests with precise diagnostic criteria. However, there is no surefire way to identify PD. Furthermore, other medical problems, including arthritis and stroke, should be evaluated on subsequent visits because they can show similarly to Parkinson’s disease. Only 80.6% of PD diagnoses are correct overall. Machine learning techniques can be applied in a variety of ways to develop different methods to recognize the presence of PD in an individual, as well as identify it in the initial stages of the disease, to aid doctors in the diagnosis of PD. The primary objective of this SLR is to explore the different methods in which features and machine learning systems can be combined to detect individuals with Parkinson’s disease (PD). We gathered, summarized, and analyzed research publications on PD detection using voice and speech datasets (and closely related topics) published between the years 2016 and 2022 for this SLR. To search for all the publications related to the issue, articles ...