Prediction of chronic liver disease patients using integrated projection based statistical feature extraction with machine learning algorithms

Amin, Ruhul; Yasmin, Rubia; Ruhi, Sabba; Rahman, Md. Habibur; Reza, Md. Shamim

doi:10.1016/j.imu.2022.101155

Cited by 25 publications

(3 citation statements)

References 16 publications

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“…The proposed framework demonstrated superior performance compared to various other research works, most of which utilized basic ML techniques. For instance, Amin et al [7] attained an accuracy of 91.40% by the ensemble classification algorithm. Additionally, the authors of [9] obtained an accuracy of 73.07% using only RF.…”

Section: Resultsmentioning

confidence: 99%

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Integrated Ensemble Learning Framework for Predicting Liver Disease

Ardchir,

Ouassit,

Ounacer

et al. 2023

Int. J. Onl. Eng.

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The liver disease has become a pressing global issue, with a sharp increase in cases reported worldwide. Detecting liver disease can be difficult as it often has few noticeable symptoms, which means that by the time it is detected, it may have already progressed to an advanced stage, resulting in many people dying without even realizing they had it. Early detection is crucial as it enables patients to begin treatment earlier, which can potentially save their lives. This study aimed to assess the efficacy of five ensemble machine learning (ML) models, namely RF, XGBoost, Extra Trees, bagging, and stacking methods, in predicting liver disease. It uses the ILPD dataset. To prevent overfitting and biases in the dataset, several pre-processing statistical techniques were employed to handle missing data, outliers, and data balancing. The study’s results underline the importance of using the RFE feature selection method, which allowed the use of only the most relevant features for the model, which may have improved the accuracy and efficiency of the model. The study found that the highest testing accuracy of 93% was achieved by the proposed model, which utilized an improved preprocessing approach and a stacking ensemble classifier with RFE feature selection. The use of ensemble ML has given promising results. Indeed, medical professionals can develop models better equipped to handle the complexity and variability of medical data, resulting in more accurate diagnoses, more effective treatment plans, and better patient outcomes.

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Section: Resultsmentioning

confidence: 99%

“…Amin et al [7] have proposed a study aimed at classifying patients with liver disease based on the extraction of integrated features. The method begins with a pre-processing step to eliminate missing values and replace outliers, followed by the extraction of the features most significant for classification.…”

Section: Ardchir Et Almentioning

confidence: 99%

Integrated Ensemble Learning Framework for Predicting Liver Disease

Ardchir,

Ouassit,

Ounacer

et al. 2023

Int. J. Onl. Eng.

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“…There are lots of fields where ML is extensively used, including computer vision, medical imaging, chemistry, physics, etc. [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Fine-Tuned Hybrid Stacked CNN to Improve Bengali Handwritten Digit Recognition

et al. 2023

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Recognition of Bengali handwritten digits has several unique challenges, including the variation in writing styles, the different shapes and sizes of digits, the varying levels of noise, and the distortion in the images. Despite significant improvements, there is still room for further improvement in the recognition rate. By building datasets and developing models, researchers can advance state-of-the-art support, which can have important implications for various domains. In this paper, we introduce a new dataset of 5440 handwritten Bengali digit images acquired from a Bangladeshi University that is now publicly available. Both conventional machine learning and CNN models were used to evaluate the task. To begin, we scrutinized the results of the ML model used after integrating three image feature descriptors, namely Binary Pattern (LBP), Complete Local Binary Pattern (CLBP), and Histogram of Oriented Gradients (HOG), using principal component analysis (PCA), which explained 95% of the variation in these descriptors. Then, via a fine-tuning approach, we designed three customized CNN models and their stack to recognize Bengali handwritten digits. On handcrafted image features, the XGBoost classifier achieved the best accuracy at 85.29%, an ROC AUC score of 98.67%, and precision, recall, and F1 scores ranging from 85.08% to 85.18%, indicating that there was still room for improvement. On our own data, the proposed customized CNN models and their stack model surpassed all other models, reaching a 99.66% training accuracy and a 97.57% testing accuracy. In addition, to robustify our proposed CNN model, we used another dataset of Bengali handwritten digits obtained from the Kaggle repository. Our stack CNN model provided remarkable performance. It obtained a training accuracy of 99.26% and an almost equally remarkable testing accuracy of 96.14%. Without any rigorous image preprocessing, fewer epochs, and less computation time, our proposed CNN model performed the best and proved the most resilient throughout all of the datasets, which solidified its position at the forefront of the field.

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Advancing Medical Predictive Models with Integrated Approaches

Kumar,

Singh

2024

Communications in Computer and Information Science

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Prediction of chronic liver disease patients using integrated projection based statistical feature extraction with machine learning algorithms

Cited by 25 publications

References 16 publications

Integrated Ensemble Learning Framework for Predicting Liver Disease

Integrated Ensemble Learning Framework for Predicting Liver Disease

A Fine-Tuned Hybrid Stacked CNN to Improve Bengali Handwritten Digit Recognition

Advancing Medical Predictive Models with Integrated Approaches

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