Prediction of Parkinson’s Disease Depression Using LIME-Based Stacking Ensemble Model
Depression symptoms are comparable to Parkinson’s disease symptoms, including attention deficit, fatigue, and sleep disruption, as well as symptoms of dementia such as apathy. As a result, it is difficult for Parkinson’s disease caregivers to diagnose depression early. We examined a LIME-based stack...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/3/708 |
| _version_ | 1827759956305117184 |
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| author | Hung Viet Nguyen Haewon Byeon |
| author_facet | Hung Viet Nguyen Haewon Byeon |
| author_sort | Hung Viet Nguyen |
| collection | DOAJ |
| description | Depression symptoms are comparable to Parkinson’s disease symptoms, including attention deficit, fatigue, and sleep disruption, as well as symptoms of dementia such as apathy. As a result, it is difficult for Parkinson’s disease caregivers to diagnose depression early. We examined a LIME-based stacking ensemble model to predict the depression of patients with Parkinson’s disease. This study used the epidemiologic data of Parkinson’s disease dementia patients (EPD) from the Korea Disease Control and Prevention Agency’s National Biobank, which included 526 patients’ information. We used Logistic Regression (LR) as the meta-model, and five base models, including LightGBM (LGBM), K-nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), and AdaBoost. After cleansing the data, the stacking ensemble model was trained using 261 participants’ data and 10 variables. According to the research, the best combination of the stacking ensemble model is ET + LGBM + RF + LR, a harmonious model. In order to achieve model prediction explainability, we also combined the stacking ensemble model with a LIME-based explainable model. This explainable stacking ensemble model can help identify the patients and start treatment on them early in a way that medical professionals can comprehend. |
| first_indexed | 2024-03-11T09:34:54Z |
| format | Article |
| id | doaj.art-f669a58edb2942abbdc74afe4413ad00 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T09:34:54Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-f669a58edb2942abbdc74afe4413ad002023-11-16T17:23:27ZengMDPI AGMathematics2227-73902023-01-0111370810.3390/math11030708Prediction of Parkinson’s Disease Depression Using LIME-Based Stacking Ensemble ModelHung Viet Nguyen0Haewon Byeon1Department of Digital Anti-Aging Healthcare (BK21), Inje University, Gimhae 50834, Republic of KoreaDepartment of Digital Anti-Aging Healthcare (BK21), Inje University, Gimhae 50834, Republic of KoreaDepression symptoms are comparable to Parkinson’s disease symptoms, including attention deficit, fatigue, and sleep disruption, as well as symptoms of dementia such as apathy. As a result, it is difficult for Parkinson’s disease caregivers to diagnose depression early. We examined a LIME-based stacking ensemble model to predict the depression of patients with Parkinson’s disease. This study used the epidemiologic data of Parkinson’s disease dementia patients (EPD) from the Korea Disease Control and Prevention Agency’s National Biobank, which included 526 patients’ information. We used Logistic Regression (LR) as the meta-model, and five base models, including LightGBM (LGBM), K-nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), and AdaBoost. After cleansing the data, the stacking ensemble model was trained using 261 participants’ data and 10 variables. According to the research, the best combination of the stacking ensemble model is ET + LGBM + RF + LR, a harmonious model. In order to achieve model prediction explainability, we also combined the stacking ensemble model with a LIME-based explainable model. This explainable stacking ensemble model can help identify the patients and start treatment on them early in a way that medical professionals can comprehend.https://www.mdpi.com/2227-7390/11/3/708stacking ensemblemachine learningLIMEexplainable AIdepressionParkinson |
| spellingShingle | Hung Viet Nguyen Haewon Byeon Prediction of Parkinson’s Disease Depression Using LIME-Based Stacking Ensemble Model Mathematics stacking ensemble machine learning LIME explainable AI depression Parkinson |
| title | Prediction of Parkinson’s Disease Depression Using LIME-Based Stacking Ensemble Model |
| title_full | Prediction of Parkinson’s Disease Depression Using LIME-Based Stacking Ensemble Model |
| title_fullStr | Prediction of Parkinson’s Disease Depression Using LIME-Based Stacking Ensemble Model |
| title_full_unstemmed | Prediction of Parkinson’s Disease Depression Using LIME-Based Stacking Ensemble Model |
| title_short | Prediction of Parkinson’s Disease Depression Using LIME-Based Stacking Ensemble Model |
| title_sort | prediction of parkinson s disease depression using lime based stacking ensemble model |
| topic | stacking ensemble machine learning LIME explainable AI depression Parkinson |
| url | https://www.mdpi.com/2227-7390/11/3/708 |
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