Early detection of Parkinson’s disease through patient questionnaire and predictive modelling

Prashanth, R.; Roy, Sumantra Dutta

doi:10.1016/j.ijmedinf.2018.09.008

Cited by 63 publications

(24 citation statements)

References 57 publications

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“…, X p }, where k ≤ p denotes the number of candidates. RF restricts the input feature to be X s when minimizing the Gini impurity (1). The number k is a major parameter of RF, and it is selected by cross-validation in our implementation.…”

Section: Description Of the Methodsmentioning

confidence: 99%

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Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning

et al. 2020

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Accurately detecting Parkinson's disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing to disease-modifying therapy. Towards this end, the premotor stage in PD should be carefully monitored. An innovative deep-learning technique is introduced to early uncover whether an individual is affected with PD or not based on premotor features. Specifically, to uncover PD at an early stage, several indicators have been considered in this study, including Rapid Eye Movement and olfactory loss, Cerebrospinal fluid data, and dopaminergic imaging markers. A comparison between the proposed deep learning model and twelve machine learning and ensemble learning methods based on relatively small data including 183 healthy individuals and 401 early PD patients shows the superior detection performance of the designed model, which achieves the highest accuracy, 96.45% on average. Besides detecting the PD, we also provide the feature importance on the PD detection process based on the Boosting method.

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Section: Description Of the Methodsmentioning

confidence: 99%

“…Parkinson's disease (PD) is becoming an important degenerative disease of the central nervous system, affecting the quality of lives of millions of seniors worldwide [1]. Symptoms of PD can progress differently from one person to another because of the variety of the disease.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning

et al. 2020

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“…Sleep quality has remarkable effects on personal well-being, as sleep encompasses roughly one-third of human life [16]. A wide range of studies explore the relationship between sleep and health issues such as obesity [17], weakened immune system [18], Parkinson's disease [19,20], mortality [5], heart failure [21], and cancer [22]. Additionally, the relationship between sleep and performance of students [23], athletes [24], and workers [25] have been correlated.…”

Section: Literature Reviewmentioning

confidence: 99%

Sleep quality prediction in caregivers using physiological signals

Sadeghi

Banerjee

Hughes

et al. 2019

Computers in Biology and Medicine

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Most caregivers of people with dementia (CPWD) experience a high degree of stress due to the demands of providing care, especially when addressing unpredictable behavioral and psychological symptoms of dementia. Such challenging responsibilities make caregivers susceptible to poor sleep quality with detrimental effects on their overall health. Hence, monitoring caregivers' sleep quality can provide important CPWD stress assessment. Most current sleep studies are based on polysomnography, which is expensive and potentially disrupts the caregiving routine. To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage. This system utilizes four raw physiological signals using a wearable device (E4 wristband): heart rate variability, electrodermal activity, body movement, and skin temperature. To evaluate the performance of the proposed method, analyses were conducted on a two-week period of sleep monitored on eight CPWD. The best performance is achieved using the random forest classifier with an accuracy of 75% for sleep quality, and 73% for restfulness, respectively. We found that the most important features to detect these measures are sleep efficiency (ratio of amount of time asleep to the amount of time in bed) and skin temperature. The results from our sleep analysis system demonstrate the capability of using wearable sensors to measure sleep quality and restfulness in CPWD.

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“…Unlike R2, the RMSE should be closer to "0". RMSE = √ ∑( − ) 2 (11) where n is the number of records. Also, , x j is the actual values and x i is the values estimated by the model.…”

Section: R2 =mentioning

confidence: 99%

Hybrid Machine Learning Model of Extreme Learning Machine Radial Basis Function for Breast Cancer Detection and Diagnosis: A Multilayer Fuzzy Expert System

Mojrian¹,

Pintér²,

Joloudari³

et al. 2019

Preprint

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Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R2, MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

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Early detection of Parkinson’s disease through patient questionnaire and predictive modelling

Cited by 63 publications

References 57 publications

Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning

Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning

Sleep quality prediction in caregivers using physiological signals

Hybrid Machine Learning Model of Extreme Learning Machine Radial Basis Function for Breast Cancer Detection and Diagnosis: A Multilayer Fuzzy Expert System

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