Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

Ijaz, Muhammad Fazal; Alfian, Ganjar; Syafrudin, Muhammad; Rhee, Jongtae

doi:10.3390/app8081325

Cited by 171 publications

(101 citation statements)

References 56 publications

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“…The DBSCAN is a density-based clustering algorithm proposed by Sander, J. et al [26] in 1998, which is widely used in the fields of physics [27], computer science [28,29], medicine [30], architecture [31], agriculture [32] and so on. Compared to other clustering methods such as K-means and Gaussian mixtures, the advantages of the DBSCAN method lie in the following aspects: (1) It has better identification capability for abnormal points.…”

Section: Diagnosis Methodsmentioning

confidence: 99%

DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

Zhang

Liu

et al. 2019

Energies

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Battery system diagnosis and prognosis are essential for ensuring the safe operation of electric vehicles (EVs). This paper proposes a diagnosis method of thermal runaway for ternary lithium-ion battery systems based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. Two-dimensional fault characteristics are first extracted according to battery voltage, and DBSCAN clustering is used to diagnose the potential thermal runaway cells (PTRC). The periodic risk assessing strategy is put forward to evaluate the fault risk of battery cells. The feasibility, reliability, stability, necessity, and robustness of the proposed algorithm are analyzed, and its effectiveness is verified based on datasets collected from real-world operating electric vehicles. The results show that the proposed method can accurately predict the locations of PTRC in the battery pack a few days before the thermal runaway occurrence.

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Section: Diagnosis Methodsmentioning

confidence: 99%

DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

Zhang

Liu

et al. 2019

Energies

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“…However, the main limitation of this method is the cold-start problem, in which there is no accurate data generated when the initial input data are limited numbers. The reason is that SMOTE generates the random data in the nearest boundary of acquired data [ 36 ]. In small amounts of data, the boundary area is narrowed.…”

Section: Proposed Adaptive Data Boosting Methodologiesmentioning

confidence: 99%

Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments

Bang

Hur

Kim

et al. 2018

Sensors

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Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we propose the Robust Personalized Emotion Recognition Framework with the Adaptive Data Boosting Algorithm to solve the cold-start problem. The proposed framework incrementally provides a customized training model for the target user by reinforcing the dataset by combining the acquired target user speech with speech from other users, followed by applying SMOTE (Synthetic Minority Over-sampling Technique)-based data augmentation. The proposed method proved to be adaptive across a small number of target user datasets and emotionally-imbalanced data environments through iterative experiments using the IEMOCAP (Interactive Emotional Dyadic Motion Capture) database.

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“…Fazal proposes a Hybrid Prediction Model (HPM) [19]. This study analyzes a dataset to improve early diagnosis of Type 2 Diabetes and Hypertension.…”

Section: Related Workmentioning

confidence: 99%

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

et al. 2020

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Guillain–Barré Syndrome (GBS) is an unusual disorder where the body’s immune system affects the peripheral nervous system. GBS has four main subtypes, whose treatments vary among them. Severe cases of GBS can be fatal. This work aimed to investigate whether balancing an original GBS dataset improves the predictive models created in a previous study. purpleBalancing a dataset is to pursue symmetry in the number of instances of each of the classes.The dataset includes 129 records of Mexican patients diagnosed with some subtype of GBS. We created 10 binary datasets from the original dataset. Then, we balanced these datasets using four different methods to undersample the majority class and one method to oversample the minority class. Finally, we used three classifiers with different approaches to creating predictive models. The results show that balancing the original dataset improves the previous predictive models. The goal of the predictive models is to identify the GBS subtypes applying Machine Learning algorithms. It is expected that specialists may use the model to have a complementary diagnostic using a reduced set of relevant features. Early identification of the subtype will allow starting with the appropriate treatment for patient recovery. This is a contribution to exploring the performance of balancing techniques with real data.

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Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

Cited by 171 publications

References 56 publications

DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

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