Liver disease prediction using ML techniques

Pasha, Syed Nawaz; Ramesh, Dadi; Mohmmad, Sallauddin; Navya, P.; Kishan, P. Anil; Sandeep, C. H.

doi:10.1063/5.0081787

Cited by 8 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That is, a case included in the sample file set but not carved is indicated as a false negative. In addition, false negative can be divided into a case where the tool does not support the sample file, so it is not carved, and a case where the tool does not sup-port the sample file, but the carving process cannot be performed properly (17) . These are called "supported false negatives" and "unsupported false negatives, " respectively.…”

Section: File Carving Resultsmentioning

confidence: 99%

An Investigation on File Carving Tool Methodologies Using Scenario Based Image Creation

Suthar,

Sharma

2024

IJST

View full text Add to dashboard Cite

Section: File Carving Resultsmentioning

confidence: 99%

An Investigation on File Carving Tool Methodologies Using Scenario Based Image Creation

Suthar,

Sharma

2024

IJST

View full text Add to dashboard Cite

“…Various ML [26] approaches may examine large datasets and produce insightful results. Since ML models use a variety of methodologies, these algorithms are crucial for properly predicting whether or not cardiac abnormalities would arise [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Ensemble Stacking Techniques for Coronary Artery Disease Prediction using Machine Learning Algorithms

Sarat Kumar Chettri, HMM. Naleer

2023

IJRITCC

View full text Add to dashboard Cite

Throughout history, humanity has been plagued by several outbreaks that have claimed numerous lives. Since coronary artery disease is among the most fatal illnesses that humanity has faced in the modern era, it has been recognized in our time. It links several Coronary Artery Disease (CAD) risk factors to the critical requirement for precise, reliable, and workable methods for early identification and management. In light of this, we suggest a technique called Hybrid Ensemble Stacking that combines Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Ada Boosting for the prediction of CAD illnesses. To combine the forecasts of the basis models, a meta-logistic regression model is utilized. According to a quantitative study, the ensemble model and brute force feature selection method together produce a classification accuracy for heart disease of up to 92.66%. The suggested stacking model has demonstrated its effectiveness and outperforms current methods in the categorization of cardiac disorders. Several classification issues have been solved successfully using ensemble techniques. The suggested method was constructed using the Sani dataset, which contains 303 nearly completed records. Using Min-Max Normalization, the data are pre-processed to making it suitable for a Machine Learning (ML) model. SMOTE and SelectKBest technique were applied to increases the accuracy and efficiency of a model. Using the metrics such as accuracy, precision, recall, F1, ROC and log-loss, the outcomes produced by the suggested model had the greatest performance.

show abstract

CNN with machine learning approaches using ExtraTreesClassifier and MRMR feature selection techniques to detect liver diseases on cloud

et al. 2022

View full text Add to dashboard Cite

Liver disease prediction using ML techniques

Cited by 8 publications

References 11 publications

An Investigation on File Carving Tool Methodologies Using Scenario Based Image Creation

An Investigation on File Carving Tool Methodologies Using Scenario Based Image Creation

Hybrid Ensemble Stacking Techniques for Coronary Artery Disease Prediction using Machine Learning Algorithms

CNN with machine learning approaches using ExtraTreesClassifier and MRMR feature selection techniques to detect liver diseases on cloud

Contact Info

Product

Resources

About