Detection of Parkinson's Disease by Employing Boosting Algorithms

Nishat, Mirza Muntasir; Hasan, Tasnimul; Nasrullah, Sarker Md.; Faisal, Fahim; Asif, Md. Asfi-Ar-Raihan; Hoque, Md. Ashraful

doi:10.1109/icievicivpr52578.2021.9564108

Cited by 39 publications

(5 citation statements)

References 31 publications

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“…While the AUCs derived here can be deemed as somewhat low-moderate at 5 years before diagnosis, it should be noted that the main aim of this model is to serve as a tool to help clinicians assess probable risk of people developing PD in the future and not for diagnosis per se. Other researchers have reported higher AUCs for predictive models, however most of those models were built using large numbers of extracted features including, but not limited to, clinical variables, neuropsychological test scores, domain composite scores, brain section measurements and magnetic resonance imaging (AUC = 0.84) 34,35 , while other models relied on physical movement tests (e.g. finger tapping) to extract bradykinesia features (AUC = 0.79-0.85) or PD detection based on language and/ or speech data (AUC > 0.85) [36][37][38] .…”

Section: Discussionmentioning

confidence: 99%

Externally validated deep learning model to identify prodromal Parkinson’s disease from electrocardiogram

Karabayir

Gunturkun

Butler

et al. 2023

Sci Rep

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Little is known about electrocardiogram (ECG) markers of Parkinson’s disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case–control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Clinical data were available from May, 2014 onward at LUC and from January, 2015 onward at MLH, while the ECG data were available as early as 1990 in both institutes. PD was denoted by at least two primary diagnostic codes (ICD9 332.0; ICD10 G20) at least 30 days apart. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. ECGs obtained at least 6 months before PD incidence date were modeled to predict a subsequent diagnosis of PD within three time windows: 6 months–1 year, 6 months–3 years, and 6 months–5 years. We applied a novel deep neural network using standard 10-s 12-lead ECGs to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for sex, race and age were also performed. Our primary prediction model was a one-dimensional convolutional neural network (1D-CNN) that was built using 131 cases and 1058 controls from MLH, and externally validated on 29 cases and 165 controls from LUC. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation AUC of 0.67 when predicting future PD at any time between 6 months and 5 years after the ECG. Accuracy increased when restricted to ECGs obtained within 6 months to 3 years before PD diagnosis (AUC 0.69) and was highest when predicting future PD within 6 months to 1 year (AUC 0.74). The 1D-CNN model based on raw ECG data outperformed multiple models built using more standard ECG feature engineering approaches. These results demonstrate that a predictive model developed in one cohort using only raw 10-s ECGs can effectively classify individuals with prodromal PD in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help identify individuals with prodromal PD for cost-effective population-level early detection and inclusion in disease-modifying therapeutic trials.

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Section: Discussionmentioning

confidence: 99%

Externally validated deep learning model to identify prodromal Parkinson’s disease from electrocardiogram

Karabayir

Gunturkun

Butler

et al. 2023

Sci Rep

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“…In the iteration process, the weights of each unclassified dataset have been increased whereas the weight for correctly classified samples is gradually decreased (Asif et al, 2021). These weights indicate the accuracy of the classifier and it is a function of the overall weights of the correctly classified samples (Nishat et al, 2021). The accuracy of the algorithm depends on the given weight of correctly classified samples.…”

Section: Adaboosting (Adb) Classificationmentioning

confidence: 99%

Point and pixel inclusive machine learning models for exploring gully erosion susceptibility

Pal

Debanshi

Paul³

2022

Geocarto International

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Sample point based spatial model derived from Machine Learning (ML) algorithms often generalizes the spatial pattern of event. The present study has tried to highlight how far it is acceptable and can it replace pixel based modelling? The present study has presented a comparative view of pixel and sample point based modelling of gully erosion susceptibility of the upper Mayurakshi basin to assess the predictabilities. Random forest (RF), Support Vector Machine (SVM) and (ADB) were applied for developing pixel and point based models in WEKA and Python software environments respectively. From the models, it is found that 14-25% area located mainly in the upper parts of the study unit is very highly susceptible to gully erosion. Based on the accuracy and performance level using Area under curve (AUC) of Receiver operating curve, sensitivity, precision, F1 score, MCC pixel based ensemble models are superior to point based modelling. Often, the point based models have a very poor agreement with training and testing data. So, pixel based models could not be replaced with point based models. RF is found as the best representative model. The study, therefore, recommends using pixel based modelling for this or a similar purpose. Since the models have figured out the gully erosion susceptible areas, it would be a useful tool for related planning processes.

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“…However, clinical assessment of mass undergraduate students would be unwieldy and resource-heavy, since there can be numerous factors involved in instigating depression. In this regard, Machine Learning (ML) models [16][17][18][19][20][21][22][23][24][25] can perhaps become valuable for detecting and predicting subsequent health issues [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] as well as depressive episodes. Furthermore, the result can be analyzed to identify depression-related trends revealed among young people which can aid higher education institutions to understand the factors better and develop effective strategies to mitigate these factors.…”

Section: Introductionmentioning

confidence: 99%

Impact of mobile connectivity on students’ wellbeing: Detecting learners’ depression using machine learning algorithms

Siraji,

Rahman,

Nishat

et al. 2023

PLoS ONE

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Depression is a psychological state of mind that often influences a person in an unfavorable manner. While it can occur in people of all ages, students are especially vulnerable to it throughout their academic careers. Beginning in 2020, the COVID-19 epidemic caused major problems in people’s lives by driving them into quarantine and forcing them to be connected continually with mobile devices, such that mobile connectivity became the new norm during the pandemic and beyond. This situation is further accelerated for students as universities move towards a blended learning mode. In these circumstances, monitoring student mental health in terms of mobile and Internet connectivity is crucial for their wellbeing. This study focuses on students attending an International University of Bangladesh to investigate their mental health due to their continual use of mobile devices (e.g., smartphones, tablets, laptops etc.). A cross-sectional survey method was employed to collect data from 444 participants. Following the exploratory data analysis, eight machine learning (ML) algorithms were used to develop an automated normal-to-extreme severe depression identification and classification system. When the automated detection was incorporated with feature selection such as Chi-square test and Recursive Feature Elimination (RFE), about 3 to 5% increase in accuracy was observed by the method. Similarly, a 5 to 15% increase in accuracy has been observed when a feature extraction method such as Principal Component Analysis (PCA) was performed. Also, the SparsePCA feature extraction technique in combination with the CatBoost classifier showed the best results in terms of accuracy, F1-score, and ROC-AUC. The data analysis revealed no sign of depression in about 44% of the total participants. About 25% of students showed mild-to-moderate and 31% of students showed severe-to-extreme signs of depression. The results suggest that ML models, incorporating a proper feature engineering method can serve adequately in multi-stage depression detection among the students. This model might be utilized in other disciplines for detecting early signs of depression among people.

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Detection of Parkinson's Disease by Employing Boosting Algorithms

Cited by 39 publications

References 31 publications

Externally validated deep learning model to identify prodromal Parkinson’s disease from electrocardiogram

Externally validated deep learning model to identify prodromal Parkinson’s disease from electrocardiogram

Point and pixel inclusive machine learning models for exploring gully erosion susceptibility

Impact of mobile connectivity on students’ wellbeing: Detecting learners’ depression using machine learning algorithms

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