# Machine Learning Methods with Decision Forests for Parkinson's Detection
## 4 Materials and Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Sets** & **Measures** & **Explanation** & **Number of Features** \\ \hline \multicolumn{4}{c}{**Primary Feature Segments**} \\ \hline \multirow{4}{*}{Jitter variants} & Jitter is affected due to the lack of control of vibration of the cords and it represents the frequency from cycle to cycle. It shows the variant of the consecutive periods. & \multirow{4}{*}{5} \\ \cline{2-3}  & Shimmer radiates the amplitude alteration of the wave of sound. & & 6 \\ \cline{2-3}  & Fundamental frequency & The frequency represents the vibration of vocal & 5 \\ \cline{2-3}  & parameters & & \\ \cline{2-3}  & Harmonicity parameters & Due to partial closure of vocal fold, noise components arise in speech. Here two components via Harmonics to Noise Ratio and Noise to Harmonics Ratio parameters were used. & \multirow{4}{*}{2} \\ \cline{2-3}  & Recurrence Period Density Entropy (RPDE) & It provides evidence regarding the capacity of the vocal folds to sustain steady vocal fold fluctuations and also computes the differences. & \multirow{4}{*}{1} \\ \cline{2-3}  & Detrended Fluctuation Analysis (DFA) & DFA is used to measure the self-similarity of the noise that is generated due to the turbulent airflow in vocal fold. & \multirow{4}{*}{1} \\ \cline{2-3}  & Pitch Period Entropy (PPE) & PPE measures the impure perplexing influence of fundamental frequency. It uses the logarithmic scale and entropy calculation. & \multirow{4}{*}{1} \\ \hline \multirow{4}{*}{Time Frequency Features} & Intensity Barriers the loudness of speech signal. & It is associated to the subglottis tension of the airflow. It measures in DB. Here, minimum and maximum intensity along with mean intensity were considered. & \multirow{4}{*}{3} \\ \cline{2-3}  & Formant Frequencies & It is used to evaluate the frequency response of the vocal tract filer. & \multirow{4}{*}{4} \\ \cline{2-3}  & Bandwidth & Bandwidth frequency range among the formant frequencies. Here, first four bandwidths were considered. & \multirow{4}{*}{4} \\ \cline{2-3}  & Glottis Quotient (GQ) & It provides evidence regarding opening and closing intervals of the glottis. & \multirow{4}{*}{3} \\ \cline{2-3}  & Glottal to Noise Excitation (GNE) & It calculates the extent of chaotic noise, which affected by inadequate closing of vocal fold. & \multirow{4}{*}{6} \\ \cline{2-3}  & Vocal Fold Excitation Ratio (VFER) & It calculates the volume of noise on the vocal fold vibration using non-linear energy and entropy estimation. & \multirow{4}{*}{7} \\ \cline{2-3}  & Empirical Mode Decomposition (EMD) & It decomposes a voice signal into basic components using adaptive basis functions and strength \& entropy estimation. & \multirow{4}{*}{6} \\ \cline{2-3}  & & Strength \& entropy are calculated from these components that represent noise. & \multirow{4}{*}{6} \\ \hline \multicolumn{4}{c}{**Derived Feature Segments**} \\ \hline Mel Frequency & MFCCs & MFCCs are applied to PD voice signal to calculated Mel Frequency features. & \multirow{4}{*}{84} \\ \hline Wavelet Transform & Wavelet transform (WT) & Wavelet Transformation is carried out to find out the WT features set on the voice signal. & \multirow{4}{*}{182} \\ \cline{2-3}  & features related with F0 & & \\ \hline Tunable Q-Factor & Tunable Q-Factor wavelet transform (TQWT) features & To quantify the deviations in from fundamental frequencies with tunable Q-factor related with F0 & \multirow{4}{*}{432} \\ \cline{2-3}  & Total Number of features & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: General structure of Istanbul acoustic dataset. The acoustic features analysis through decision forests has been conducted in two stages. At first, three decision forest algorithms Random Forest, SysFor and ForestPA has been subjected for comparison along with six state-of-the-art supervised classifiers, viz., k-Nearest Neighbor (kNN), C4.5, Logistic Regression, Back-Propagation Neural Network (BPNN), Naive Bayes and OneR.