Table 3.
ML algorithms developed for the classification of PD over the past ten years and their accuracies
| Reference | Title | Method | Architecture | Accuracy |
|---|---|---|---|---|
| Mancini et al., 2011 | Trunk accelerometry reveals postural instability in untreated PD | Posture: (i) EO gazes straight ahead at art poster 6 m continuously. (ii) EC, upright standing position; (iii) EC cognitive task (ECT). | Linear mixed model; ROC | Accuracy for F95: 0.90; FD: 0.82; RMS: 0.93; for jerk (EO) for untreated PD/HC classification. |
| Sant’Anna et al., 2011 | New measure of movement symmetry in early PD patients using symbolic processing of inertial sensor data | Walking 30 m hallway at preferred speed (2 minutes) | t-test, ROC; ICC | Accuracy for PD/HC classification: 0.872; ICC: 0.949 |
| Rigas et al., 2012 | Assessment of tremor activity in PD using set of wearable sensors | Resting task (resting in bed, on a chair, standing with hand support); Postural task; kinetic tasks (finger to nose, finger to finger, walking, and picking) | HMM (Leaving one patient out) | Accuracy for posture and action detection: 81%; Accuracy for tremor severity classification: 87% |
| Scanlon et al., 2013 | Accelerometry-based study of lower and upper limb tremors in PD | Resting task, Postural task with distracting task [upper and lower limbs, both dexterity dominant and non-dominant] (each 8.2 seconds) | Mann-Whitney U and Wilcoxon signed-rank texts | IIVF50 for RT (P = 0.032), (P = 0.017) lower in the DD lower limb of PwPD compared to HC |
| Chen et al., 2014 | Postural sway in idiopathic rapid eye movement sleep behavior disorder as a potential marker of prodromal Parkinson’s disease | Upright standing position: arms crossed by chest, looking ahead (every 30 seconds) (i) eyes open (EO) (ii) feet together eyes closed (EC) (iii) feet together EO dual-task (EODT) (iv) feet together EC dual-task (ECDT) (v) tandem standing EO (TEO) | ANOVA, t-test, Pearson chi-square test | Differences in jerk between PD/HC for EODT (P = 0.030), ECDT (P = 0.015), and TEO (P = 0.023) |
| Kostikis et al., 2015 | Smartphone-based tool for assessing Parkinsonian hand tremor | Resting and Postural task (every 30 seconds) | Pearson coefficient; Bag DT | AUC for PwPD/HC classification: 0.94 |
| Perumal and Sankar, 2016 | Gait and tremor assessment for patients with PD using wearable sensors | Gait and Tremor (60 seconds) | ANOVA, LDA 5-fold cross-validation, ROC | Mean accuracy for Gait: 91.58%, ROC: 0.72 AUC for PD/HC classification: 90% Features able to differentiate PD tremor from atypical PD tremor |
| Cai et al., 2017 | New hybrid intelligent framework for predicting PD | Voice recordings of 31 subjects, including 23 PD patients (16 males, 7 females) and 8 healthy controls (3 males, 5 females). Each subject provided an average of six 36-second long phonations of vowels (95 samples total) | BFO-SVM, KELM | Acc of BFO-SVM: 96.84%, sensitivity: 98.75%, Specificity: 90.83% |
| Rumman et al., 2018 | Early detection of PD using image processing and artificial neural networks | SPECT Image dataset retrieved from PPMI database. ANN trained twice: first with ROI area of known subjects, then ROI area of unknown subjects | ANN | Accuracy: 94% Sensitivity: 100% Specificity: 88% |
| Woodzinski et al., 2019 | Deep learning approach to PD detection using voice recordings and convolutional neural networks for image classification | 100 voice recordings divided into 10 folds 90/10% (training and validation data). Included 50 HC and 50 PD patient recordings. PC-GITA database created to evaluate the model | LSTM, ResNet with 18 layers | F1-score, Precision, and recall: 0.92 Accuracy: 0.917 |
| Nair et al., 2020 | Predicting early-stage drug-induced Parkinsonism using unsupervised and supervised machine learning | Kinematic walking data | Logistic regression model | Logistic regression accuracy: 0.94, specificity: 0.96, sensitivity: 0.89 |
| Powers et al., 2021 | Longitudinal, remote smartwatch monitoring of PD motor defects to inform treatment decisions | Motor Fluctuations Monitor for Parkinson’s Disease (MM4PD) algorithm trained on smartwatch tremor data from 3 studies (343 PD, 171 controls), used to track symptom changes with activity, medication, etc. | Apple Proprietary Algorithm | Post-treatment symptom changes matching clinical expectations: 94% |