Optimizing Liver disease prediction with Random Forest by various Data balancing Techniques

Ambesange, Sateesh; Vijayalaxmi, A; Uppin, Rashmi; Patil, Shruthi; Patil, Vilaskumar

doi:10.1109/ccem50674.2020.00030

Cited by 18 publications

(5 citation statements)

References 5 publications

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“…The experimental results showed that CNN's accuracy in predicting prevalent diseases reached 84.5%. Ambesange et al used the Indian Liver Patient Dataset (ILPD) to build a disease prediction model based on the random forest (RF) algorithm [17], and the model prediction accuracy reached 100%. Zhao et al used the liver function records of 573 patients and proposed the W-LR-XGB algorithm for the prediction model of liver disease patients [18].…”

Section: Related Workmentioning

confidence: 99%

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Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Zhou

et al. 2023

Electronics

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With the innovation of technologies such as sensors and artificial intelligence, some nursing homes use wearable devices to monitor the movement and physiological indicators of the elderly and provide prompts for any health risks. Nevertheless, this kind of risk warning is a decision based on a particular physiological indicator. Therefore, such decisions cannot effectively predict health risks. To achieve this goal, we propose a model Lidom (A LightGBM-based Disease Prediction Model) based on the combination of the LightGBM algorithm, InterpretML framework, and sequence confrontation network (SeqGAN). The Lidom model first solves the problem of uneven samples based on the sequence confrontation network (SeqGAN), then trains the model based on the LightGBM algorithm, uses the InterpretML framework for analysis, and finally obtains the best model. This paper uses the public dataset MIMIC-III, subject data, and the early diabetes risk prediction dataset in UCI as sample data. The experimental results show that the Lidom model has an accuracy rate of 93.46% for disease risk prediction and an accuracy rate of 99.8% for early diabetes risk prediction. The results show that the Lidom model can provide adequate support for the prediction of the health risks of the elderly.

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Section: Related Workmentioning

confidence: 99%

“…Atkov [6] Coronary Heart Disease √ Neural Networks Hatim [7] Chronic Renal Failure √ SVM Junyi [15] Population Disease Prediction √ Attention network, Neural network Dahiwade [16] Early Disease Prediction √ KNN, CNN Ambesange [17] Zhao [18] Liver Disease √ Random Forest, XGBoost…”

Section: Integrated Disease Risk Prediction Algorithmmentioning

confidence: 99%

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Zhou

et al. 2023

Electronics

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“…The experimental results showing that the designed diagnostic system can valid predict the risk of heart disease, It can help relevant personnel in the medical field to develop better early diagnosis plans for patients. [5] Univariate and bivariate analysis are used to check the skewness and outliers of the data set, then appropriate algorithms are used to take out exception value, and kinds oversampling and undersampling techniques are used to balance the data. Finally, the model is further optimized through the super parameter optimization of grid search.…”

Section: Related Workmentioning

confidence: 99%

Study on heart disease prediction based on SVM-GBDT hybrid model

2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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In the past ten years, heart disease has been the main cause of death among Chinese residents. At present, the more accurate way to diagnose heart disease is invasive examination - cardiac angiography. This diagnostic method may cause serious arrhythmia, and some people may be allergic to contrast agents. Therefore, certain manpower and material resources are required to monitor the patient's vital signs after angiography. So, if we can use other patient data information to predict whether a person has heart disease through machine learning, it will make a great contribution to the prevention and diagnosis of heart disease. For this reason, this paper proposes an SVM-GBDT hybrid model based on feature selection to predict the occurrence of heart disease. After data processing, the regression results are obtained from the SVM model, and then the important attributes are filtered through feature selection by setting variance thresholds. The regression results are combined with the results of feature selection, and the GBDT model is used for prediction analysis. The experimental results show that the svm-gbdt hybrid model presented in this paper performs better than the single model at multiple evaluation metrics. When compared with the prediction effect of other machine learning models, the hybrid model proposed in this paper also performs well. As a result, the SVM-GBDT hybrid model based on feature selection proposed in this paper can play a helpful role in the prediction and diagnosis of heart disease.

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“…The problem of data imbalance in medical datasets is discussed in [62]. Class balancing methods are applied in Diabetes dataset and Liver disease datasets in [63] and [64] respectively to increase the F1 score for the minority classes.…”

Section: Related Workmentioning

confidence: 99%

Increasing the reliability of Heart Disease classification models using Class Balancing techniques with Feature Engineering

Sharma,

Lalwani

2023

Preprint

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In this article, we explore the natural problem of class imbalance in the heart disease datasets. Aiming for a comprehensive examination of the class balancing techniques we train and test our models on three different datasets all suffering from different degree of class imbalance. The Healthcare dataset (mid size) and the BRFSS-2015 dataset (large dataset) having huge class imbalance, and the Iraq hospital dataset with mild class imbalance. Feature selection is done using backward elimination and Logistic Regression (LR), Adaptive Boosting (ADB), XGBoost (XGB), Random Forest (RF), and Ensemble classifiers are combined with the four popular class balancing techniques namely Random Under Sample, Random Over Sample, SMOTETomek, and ADASYN. As proven by results, Random Under Sampling and Random Over Sampling combined with Logistic Regression and ADA Boost proves the best combination for the highly imbalanced Healthcare Stroke Dataset and BRFSS-2015 dataset by producing almost 15%-20% increase in the in F1-Score of the minority class and AU-ROC making the classifier model more robust but with a minor decline in the overall accuracy. For the mildly imbalanced Iraq Hospital dataset the same combination marginally improves the F1-Score and AU-ROC without compromising on the overall accuracy and the F1-Score of majority class.

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Optimizing Liver disease prediction with Random Forest by various Data balancing Techniques

Cited by 18 publications

References 5 publications

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Study on heart disease prediction based on SVM-GBDT hybrid model

Increasing the reliability of Heart Disease classification models using Class Balancing techniques with Feature Engineering

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