Stroke Disease Analysis and Classification Using Decision Tree and Random Forest Methods

Puspitasari, Desy Ika; Kholdani, Al Fath Riza; Dharmawati, Adani; Rosadi, Muhammad Edya; Dhuhita, Windha Mega Pradnya

doi:10.1109/icic54025.2021.9632906

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…They compared the performance of models built using linear SVM, quadratic SVM, and cubic SVM algorithms and found that linear SVM and quadratic SVM performed better on the test dataset, with an accuracy score of 95.2%. Puspitasari et al [4] compared the performance of stroke prediction models built using the random forest algorithm and the decision tree algorithm. They divided the training and test sets into five ratios of 59:50, 60:40, 70:30, 80:20, and 90:10 and did five experiments.…”

Section: Introductionmentioning

confidence: 99%

Stroke Prediction Using the Trust Evaluation with Data Leakage Avoiding

Ye,

Xu,

et al. 2023

J. Phys.: Conf. Ser.

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Early detection of the severe disease -- stroke is a key step toward effective treatment. Stroke disease data is imbalanced and normally contains the majority of negative cases (without stroke) and the minority of positive cases (stroke). Previous work has used SMOTE to deal with imbalanced data, but most researchers have implemented it for the entire dataset, which means the “answer” was silently “be told” and saved in the entire data, causing data leakage. Moreover, the previous work uses accuracy only as the metrics make the result less guaranteed. We propose a method using the SMOTE applied to the training set only and apply 13 machine learning classifiers for predicting stroke. We combine the AUC with accuracy as evaluation metrics in the stroke prediction task, which can elevate the confidence level of the assessment of results. The experiment shows that misused SMOTE and standardization can cause data leakage and the combined metrics can evaluate models with higher trustworthiness. We conclude that using our method can avoid data leakage and assess the model with higher trustworthiness.

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Section: Introductionmentioning

confidence: 99%

Stroke Prediction Using the Trust Evaluation with Data Leakage Avoiding

Ye,

Xu,

et al. 2023

J. Phys.: Conf. Ser.

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show abstract

“…However, traditional diagnostic methods, such as computerized tomography, can be costly and time-consuming, making them less efficient 4 . Therefore, machine learning techniques have been widely used in recent years as a faster and more cost-effective way of diagnosing diseases, including stroke 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 .The diverse range of studies has used artificial intelligence algorithms and machine learning techniques to diagnose stroke disease. Arslan et al(2016) 5 used three different data mining approaches, Support Vector Machine (SVM), Stochastic Gradient Boost (SGB), and Penalized Logistic Regression (PLR), to analyze a dataset consisting of 80 patients and 112 healthy individuals.…”

mentioning

confidence: 99%

“…The results obtained were acceptable for SVM and SGB models. In the study carried out by Puspitasari et al(2021) 6 , they utilized the "Stroke Prediction Dataset" containing 5110 records published on the Kaggle open database site for stroke prediction. The study aimed to investigate the effectiveness of Decision Tree (DT) and Random Forest (RF) classification algorithms, and it was found that the RF algorithm produced more accurate results compared to the DT algorithm.…”

mentioning

confidence: 99%

Design and Development of a Hybrid Evolutionary Method with a Special Selection Artificial Immune System for Stroke Prediction: A Balancing Approach

ÇELİKBAŞ,

Orman,

AKGÜNDOĞDU

2024

Preprint

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Stroke is a high-risk neurological condition caused by blockages or bleeding in the brain, leading to death or disability. This study proposes a model to address the imbalance in limited patient data.The proposed model uses the MissForest method, a Random Forest Regression algorithm, to complete missing data and an artificial immune system algorithm whose parameters are updated using the Firefly algorithm to stabilize the data. The One-Sided Selection model is used to improve the performance of the minority class.The model was evaluated in two experiments, one using all features and the other selecting the best features using the Artificial Bee Colony (ABC) algorithm. The models were trained using six different classification algorithms: CatBoost, Light Gradient Boosting Machine (LightGBMBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR). The results were presented using performance metrics. When trained using all features, the model achieved an accuracy of 77%, specificity of 44%, and sensitivity of 77%. When trained using the best features selected by the ABC algorithm, the model achieved an accuracy of 81%, specificity of 61%, and sensitivity of 81%. Compared to previous studies, the proposed model was effective in both experiments.

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Unveiling Hidden Patterns: Exploration of Data Dependencies in Disease Prediction

2023

2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS)

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Stroke Disease Analysis and Classification Using Decision Tree and Random Forest Methods

Cited by 7 publications

References 25 publications

Stroke Prediction Using the Trust Evaluation with Data Leakage Avoiding

Stroke Prediction Using the Trust Evaluation with Data Leakage Avoiding

Design and Development of a Hybrid Evolutionary Method with a Special Selection Artificial Immune System for Stroke Prediction: A Balancing Approach

Unveiling Hidden Patterns: Exploration of Data Dependencies in Disease Prediction

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