Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

Several millions of people suffer from Parkinson’s disease globally. Parkinson’s affects about 1% of people over 60 and its symptoms increase with age. The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners, but which could be analyzed using recorded speech signals. With the huge advancements of technology, the medical data has increased dramatically, and therefore, there is a need to apply data mining and machine learning methods to extract new knowledge from this data. Several classification methods were used to analyze medical data sets and diagnostic problems, such as Parkinson’s Disease (PD). In addition, to improve the performance of classification, feature selection methods have been extensively used in many fields. This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based. The dataset includes 240 recodes with 46 acoustic features extracted from 3 voice recording replications for 80 patients. The experimental results showed improvements when wrapper-based features selection method was used with KNN classifier with accuracy of 88.33%. The best obtained results were compared with other studies and it was found that this study provides comparable and superior results

Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice

Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 307 dysphonia measures that quantify different properties of voice impairment, such as, breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor. We used feature selection algorithms to identify robust parsimonious feature subsets, which were used in combination with a Random Forests (RF) classifier to accurately distinguish PD from HC. The best 10-fold cross-validation performance was obtained using Gram-Schmidt Orthogonalization (GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD 2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is a step forward towards assessing the development of a reliable, cost-effective and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.Comment: 43 pages, 5 figures, 6 table

Developing a methodology for manipulating spontaneous blinks.

While blinking is necessary for ocular protection and lubrication, people blink much more than is necessary for routine ocular maintenance. These extra, spontaneous blinks are extremely difficult to manipulate and thus, have remained somewhat of a mystery. In order to determine the effects of spontaneous blinks, a methodology to manipulate them naturally must be created. The aim of this study was to develop such methodology using videos of animated speakers displaying high and low blink rates, and determine whether this influenced participant blink rates. It was expected that watching videos of a speaker's face would manipulate blink rate. It was also expected that participants would imitate the speaker's blink timing and blink immediately after the speaker blinks, called blink entrainment. Participants watched four videos, two featuring an animated speaker with a high blink rate, and two featuring the same animated speaker with a low blink rate. In between the speaker videos, participants completed ten trials of several variations of a lexical decision task. The speaker videos provided instructions on how to complete each of these tasks. A Wilcoxon signed-rank test showed that the differences between participant blink rates across the high blink rate and the low blink rate were significant (Z = -3.16, p = .002). Participants blinked more frequently while watching the high blink rate videos than when watching the low blink rate videos. A Wilcoxon signed-rank test also showed a significant difference between entrainment blinks and non-entrainment blinks in the high blink rate condition (Z = -3.65, p = .001), and the low blink rate condition (Z = -2.21, p = .027). These results indicate that a standardized methodology for manipulating spontaneous blinks is possible. With the use of the animated speaker videos, spontaneous blinks can be manipulated

Dementia in Parkinson’s Disease

An estimated 50% to 80% of individuals with Parkinson’s disease experience Parkinson’s disease dementia (PDD). Based on the prevalence and clinical complexity of PDD, this book provides an in-depth update on topics including epidemiology, diagnosis, and treatment. Chapters discuss non-medical therapies and examine views on end-of-life issues as well. This book is a must-read for anyone interested in PDD whether they are a patient, caregiver, or doctor

Factors influencing the adoption of mobile health monitoring and care systems by the elderly living at home in South Africa: a case of Buffalo City Metropolitan Municipality.

The gradual increase of elderly people around the globe necessitates intensive dialogue amongst government, the healthcare sector and elderly communities as per MPAA 2002 resolutions. Literature identifies technology as the enabler to drive the facilitation of improved living conditions beginning with an affordable, accessible and integrated health information system (HIS). The attainment of a better quality of care to meet the elderly’s needs requires the re-engineering of current modalities. The diverse nature of South Africa is more suited to a people-based rather than a process-centric approach currently in existence. Access barriers, affordability, the digital divide, lack of government buy-in, and fragmented HIS are considered major impediments to adoption of mobile monitoring and care systems (MMCs) for the elderly’s healthcare. Given the complications brought about by the Covid-19 pandemic, the adoption of MMCs cannot be more pronounced. However, despite available literature regarding elderly issues in both developed and developing countries, the elderly plight has still not been considered a national priority. The main purpose of this research was to investigate why elderly people do not adopt MMCs to improve their quality of life, with MMC technologies as a general area of research. The main objective of the study was to develop critical success factors to improve the adoption of MMCs by the elderly living at home. This would potentially alleviate the burden on healthcare resources and also improve the elderly’s quality of life. Primary data collection took place from 21 February to 28 February 2020 in Buffalo City Metropolitan Municipality. Semi-structured interviews were conducted with 15 participants comprising one male and 14 females who represented the elderly Black, Coloured, Indian and White people. This qualitative research tool and purposive sampling method were chosen in order to fully capture the participants’ experiences in the home environment, which excluded those living in frail care or step-down facilities or state institutions. Despite the sample size being small and not being generalizable, it delivered rich information which provided a deeper understanding and fresh insights into the landscape of the elderly and their healthcare needs. The interviews were recorded, transcribed and analysed thematically. The study found that elderly communities are not entirely averse to adoption of MMCs but challenges like affordability and chronic shortage of technical skills prove to be impediments to adoption of MMCs for the elderly’s healthcare. The lack of standardisation and data governance pertaining to data sharing in HISs also serve to exacerbate the matter. The study, therefore, recommends collaborative engagements amongst government, business and the elderly to facilitate the availability of affordable and accessible ICT infrastructure for the elderly communities. Improved adoption of MMCs carry the potential benefit which emanates from the assumption of a pro-active role by the elderly and optimising available MMCs thus reducing strain and freeing-up healthcare workers to concentrate on core duties. The onus thus falls on the healthcare sector to revise the available strategies which seek to enhance the quality of life of the elderly people living in the home environment.Thesis (MCom) (Information Systems) -- University of Fort Hare, 2021