Machine Learning Approaches in Parkinson’s Disease

Abstract

Background:: Parkinson’s disease is the second most frequent neurodegenerative disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At present, no biomarker is available to obtain a diagnosis of certainty in vivo. Objective:: The present review aims at describing machine learning algorithms as they have been variably applied to different aspects of Parkinson’s disease diagnosis and characterization. Methods:: A systematic search was conducted on PubMed in December 2019, resulting in 230 publications obtained with the following search query: “Machine Learning” “AND” “Parkinson Disease”. Results:: the obtained publications were divided into 6 categories, based on different application fields: “Gait Analysis - Motor Evaluation”, “Upper Limb Motor and Tremor Evaluation”, “Handwriting and typing evaluation”, “Speech and Phonation evaluation”, “Neuroimaging and Nuclear Medicine evaluation”, “Metabolomics application”, after excluding the papers of general topic. As a result, a total of 166 articles were analyzed, after elimination of papers written in languages other than English or not directly related to the selected topics. Conclusion:: Machine learning algorithms are computer-based statistical approaches which can be trained and are able to find common patterns from big amounts of data. The machine learning approaches can help clinicians in classifying patients according to several variables at the same time.

Prediction Model of Immunosuppressive Medication Non-adherence for Renal Transplant Patients Based on Machine Learning Technology

“…Machine learning (ML), which itself is a subset of a broader universe of computational learning in artificial intelligence, is now embedded in many aspects of health care processes, including biomedical research and health care delivery (9,10). There were already some good examples of using ML technology to build accurate prediction models in the medical field.…”

Artificial Intelligence in Translational Medicine

“…Similarly, similar procedures, extracting specific features from available data allowing the development of ML based models for Parkinson's disease (PD). In fact, as reported for AD, several studies highlighted that through ML-based approaches applied to PD [268] is possible to predict the progression of the disorder employing serum cytokines [269], MRI [270], and walking tests [271], to estimate the state of PD, employing longitudinal data [272], to rate the main synthomps (resting tremor and bradykinesia) [273], to produce a correct diagnosis from EEG analysis [274,275] and from voice dataset [276,277], only for reporting some relevant works.…”

Artificial Intelligence in Translational Medicine

“…Similarly, comparable procedures, extracting specific features from available data, allowing the development of ML-based models for Parkinson's disease (PD). In fact, as reported for AD, several studies highlighted that through ML-based approaches applied to PD [279], it is possible to predict the progression of the disorder by employing serum cytokines [280], MRI [281], and walking tests [282]; to estimate the state of PD, employing longitudinal data [283]; to rate the main symptoms (resting tremor and bradykinesia) [284]; and to produce a correct diagnosis from EEG analysis [285,286] and from voice datasets [287,288].…”