An effective approach for early liver disease prediction and sensitivity analysis

Khan, Md. Ashikur Rahman; Afrin, Faria; Prity, Farida Siddiqi; Ahammad, Ishtiaq; Fatema, Sharmin; Prosad, Ratul; Hasan, Mohammad Kamrul; Uddi̇n, Mai̇n; Salehin, Zayed Us

doi:10.1007/s42044-023-00138-9

Cited by 11 publications

(4 citation statements)

References 56 publications

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“…Exploring more sophisticated machine learning models like deep learning (e.g., Convolutional Neural Networks (CNNs) combined with Long Short-Term Memory networks (LSTMs)) can lead to better accuracy [8]; (iv) Ensemble Methods: Combining predictions from multiple models (e.g., using a Voting classi er) can improve accuracy by leveraging the strengths of individual models [9]; (v) Cross-validation:…”

Section: Accuracy Of Supervised Learning Algorithms For Liver Disease...mentioning

confidence: 99%

“…Using techniques like k-fold cross-validation helps in assessing the model's performance more reliably and ensures that it generalizes well to unseen data; (vi) Regularization: Implementing regularization methods to prevent over tting, which can improve the model's performance on new, unseen data; (vii) Hyperparameter Tuning: Systematically searching for the optimal hyperparameters can ne-tune the model's ability to learn from the data; and (viii) Clinical Validation: Collaborating with healthcare professionals to validate the models in a clinical setting can provide feedback for further re nement. By focusing on these areas, the accuracy and reliability of liver disease detection algorithms can be enhanced, leading to better diagnostic tools in healthcare [7][8][9].…”

Section: Accuracy Of Supervised Learning Algorithms For Liver Disease...mentioning

confidence: 99%

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Automatic Detection of Liver Diseases based on Supervised Learning Algorithms

Ahmed,

Bouallegue,

Khattab

et al. 2024

Preprint

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The incidence of Liver disease has been steadily rising due to the excessive consumption of alcohol, exposure to harmful gases through inhalation, consumption of contaminated food, and the use of certain drugs. Dataset was used that extracted from ultrasound images for liver, and some chemical compounds (bilirubin, albumin, proteins, alkaline phosphatase) present in human body as features, to build and evaluate a classifier model, it can classify and differentiate liver diseases. This paper with the help of machine learning technique proposes diagnosing and classifies liver diseases into liver patient (abnormal) and non-liver patient (normal), then classify the liver patient into fatty liver or cirrhosis, using many supervised learning algorithms. The findings indicated that the Random Forest (RF) algorithm exhibited superior accuracy, achieving an impressive 95.12% accuracy rate. Following closely was the Decision Tree (DT) classifier, achieving a respectable accuracy of 90.24%. These results were obtained after employing a data reduction technique involving resampling. The model's performance was subsequently assessed using a 10-fold cross-validation (CV) approach, widely regarded as the optimal method for classifier evaluation. This approach leverages resampling across various folds of the dataset during multiple iterations, enhancing the classifier's ability to generalize and consequently yielding elevated accuracy when applied to unlabeled image samples.

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Section: Accuracy Of Supervised Learning Algorithms For Liver Disease...mentioning

confidence: 99%

Section: Accuracy Of Supervised Learning Algorithms For Liver Disease...mentioning

confidence: 99%

Automatic Detection of Liver Diseases based on Supervised Learning Algorithms

Ahmed,

Bouallegue,

Khattab

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This is our model's capacity to identify every re. Thus, we will use the abbreviations TP (True Positive), FN (False Negatives) and FP (False Positives) to calculate the "Sensitivity" [13], the "Sensitivity" and the "Precision" which are the four main terminologies of the matrix of confusion.…”

Section: Criteria To Optimize and Derived Valuesmentioning

confidence: 99%

Fire prediction using Machine Learning Algorithms based on the confusion matrix

Korchi

Abatal

Essaid

2023

Preprint

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In an earlier article, we outlined the process of developing a Machine Learning project that is often complex to establish, and that the problem must be broken down into several stages to facilitate its resolution [1]. We were able to identify 5 steps that we think are the most important to tackle such a project. These 5 steps are : the definition of the problem, the preparation of the data, the choice of the right algorithms, the optimization of the results and the presentation of the final results. In this manuscript, we will propose the application of different ways of evaluating classification models through an algorithm that predicts whether there is a fire in a given location or not. We are conscious that this problem is difficult to solve, especially when we have to predict the latter when there is none, do not predict when there is really one, or not predict when there is none. The method of this study will show how to choose the right algorithm and how to Evaluate it. The experiment shows promising results obtained thanks to the classification model algorithm and confusion matrix which provide fire detection accuracy around 92.71%.

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“…The primary goal of ML approaches is to replace time-consuming, and laborious human operations with an increased degree of mechanization in the knowledge development process and therefore, they are used in various sectors like education [21][22][23], finance [24], medicine and healthcare [25,26], and clustering [27,28]. These ML solutions must be developed with domain-specific expertise.…”

Section: Introductionmentioning

confidence: 99%

Cricket Players Selection for National Team and Franchise League using Machine Learning Algorithms

Md. Robel,

Md. Ashikur Rahman Khan,

Ishtiaq Ahammad

et al. 2023

Cloud Computing and Data Science

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Cricket player selection is a crucial task for both national teams and franchise leagues. Traditionally, selectors rely on their experience and knowledge to evaluate a player's physical fitness, batting, and bowling performance. However, with the advancements in machine learning algorithms, it is possible to automate and improve the selection process. In this research, a Machine Learning (ML)-based approach is proposed for cricket player selection. This approach uses a combination of physical fitness data, batting and bowling statistics, and other relevant metrics to create a comprehensive player profile. Then three ML algorithms-linear regression, support vector regression, and random forests-are employed to identify the most promising players. Data is collected on a large number of cricket players and their performance in national and franchise leagues from the two most prominent cricket websites, espncricinfo, and cricbuzz, to evaluate the proposed approach. Then the proposed approach trained and tested the ML models on this data and compared their accuracy and performance. Based on the performance scores obtained from these models, two squads are selected for the national team and one squad for each franchise league team. The results demonstrate that the proposed approach can significantly improve the selection process and identify players with high potential. Furthermore, results found that the support vector regression algorithm outperformed other ML models in terms of prediction and player selection.

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An effective approach for early liver disease prediction and sensitivity analysis

Cited by 11 publications

References 56 publications

Automatic Detection of Liver Diseases based on Supervised Learning Algorithms

Automatic Detection of Liver Diseases based on Supervised Learning Algorithms

Fire prediction using Machine Learning Algorithms based on the confusion matrix

Cricket Players Selection for National Team and Franchise League using Machine Learning Algorithms

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