Parkinson’s disease is a neuro-degenerative disorder which affects quality of life of an estimated 10 million people worldwide3 . A tell-tale marker of this disease is a decrease in the dopamine levels in the brain which could be attributed to the degeneration of dopaminergic neurons. The onset of the disease may be suggested by tremor, rigidity, slowness of movement and postural instability14. Such symptoms may not present itself in the same format in all the cases but rather vary in combinations and severity but generally is chronic and degenerative. What interests us here is that among all diagnosed cases of PD, 90% of the cases show some sort of vocal impairment which consists of deterioration of normal production of vocal sounds, which is medically termed as Dysphonia
As PD patients show unique and characteristic vocal features, their voice recordings would prove to be an invaluable procedure for diagnosis as it is also non-invasive. Hence tests using speech prove to be a good tool to audit PD. This opens up an entirely new door of opportunities for Machine Learning algorithms to work on in-house voice recording datasets of PD patients and then go on to efficiently predict or diagnose PD20 . Voice features from the audio clips can be extracted by passing the recordings through signal processing algorithms and a these features can be used to build a classification and regression model to predict a rating on the Unified Parkinson’s Disease Rating Scale (UPDRS)15. UPDRS, which indicates presence and progression of PD, has been as the most commonly used scale for measuring and assessing PD.
The dataset is taken from the UCI ML repository. Exploratory data analysis is done long with visualizations of attributes to understand how the data characteristics of the data
The dataset used in this study is collection of features from voice recordings taken from 42 candidates who have been diagnosed with early-stage Parkinson's disease recruited to a six-month trial of a telemonitoring device for remote symptom progression monitoring. These recordings were captured by the device at the homes of the patients. Each row in the dataset corresponds to one of 5,875 voice recording from the aforementioned individuals. The objective of this study is to predict the motor and total UPDRS scores ('motor_UPDRS' and 'total_UPDRS') from the 16 vocal attributes which will give an idea about the progression of PD. The attributes in the dataset: (i) Subject number: Unique Id of each individual (ii) Subject Age: Age of the patient (iii) Subject gender: ‘0’ indicates male, ‘1’ indicates female (iv) Test time: Time since the individual has been recruited into the trial. (v) UPDRS: This is a clinician’s scale for recording symptoms related to Parkinson’s disease. The UPDRS metric is a scale with 44 sections, and each of those sections addresses a symptom at one different part of the body. A summation of these 44 section will yield a value called UPDRS score which ranges between 0 (perfectly healthy) to 176(total disability) (vi) Motor UPDRS: Motor UPDRS score given by the clinician, linearly interpolated - this forms sections 18-44 from the UPDRS sections (vii) Total_UPDRS: total UPDRS score given by clinician, linearly interpolated - this includes all 44 sections. (viii) 16 other biomedical voice measures such as : Jitter Percentage, Jitter (Absolute), Jitter (RAP), Jitter (PPQ5), Jitter (DDP), Shimmer, Shimmer(dB), Shimmer:APQ3, Shimmer:APQ5, Shimmer:APQ11, Shimmer: DDA, NHR, HNR, RPDE, DFA, PPE
Prediction: The objective of this study is to accurately predict the motor and total UPDRS scores ('motor_UPDRS' and 'total_UPDRS') from the 16 voice measures using various machine learning methods and compare the results. For the same purpose we will be making use of SVM, Random Forest, Linear Regression and Multi-Variate Linear regression. We’ve used Root Mean Squared Error (RMSE). RMSE is defined as the square root of the average of the square of the total error. It’s one of the most common uses measure of accuracy for prediction problems.