Lung Cancer Risk Prediction with Machine Learning Models

Big Data and Cognitive Computing, Journal Year: 2022, Volume and Issue: 6(4), P. 139 - 139

Published: Nov. 15, 2022

The lungs are the center of breath control and ensure that every cell in body receives oxygen. At same time, they filter air to prevent entry useless substances germs into body. human has specially designed defence mechanisms protect lungs. However, not enough completely eliminate risk various diseases affect Infections, inflammation or even more serious complications, such as growth a cancerous tumor, can In this work, we used machine learning (ML) methods build efficient models for identifying high-risk individuals incurring lung cancer and, thus, making earlier interventions avoid long-term complications. suggestion article is Rotation Forest achieves high performance evaluated by well-known metrics, precision, recall, F-Measure, accuracy area under curve (AUC). More specifically, evaluation experiments showed proposed model prevailed with an AUC 99.3%, recall 97.1%.

Language: Английский

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Supervised Machine Learning Models for Liver Disease Risk Prediction

Computers, Journal Year: 2023, Volume and Issue: 12(1), P. 19 - 19

Published: Jan. 13, 2023

The liver constitutes the largest gland in human body and performs many different functions. It processes what a person eats drinks converts food into nutrients that need to be absorbed by body. In addition, it filters out harmful substances from blood helps tackle infections. Exposure viruses or dangerous chemicals can damage liver. When this organ is damaged, disease develop. Liver refers any condition causes may affect its function. serious threatens life requires urgent medical attention. Early prediction of using machine learning (ML) techniques will point interest study. Specifically, content research work, various ML models Ensemble methods were evaluated compared terms Accuracy, Precision, Recall, F-measure area under curve (AUC) order predict occurrence. experimental results showed Voting classifier outperforms other with an accuracy, recall, 80.1%, precision 80.4%, AUC equal 88.4% after SMOTE 10-fold cross-validation.

Language: Английский

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Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction

Sensors, Journal Year: 2023, Volume and Issue: 23(3), P. 1161 - 1161

Published: Jan. 19, 2023

Cardiovascular diseases (CVDs) are now the leading cause of death, as quality life and human habits have changed significantly. CVDs accompanied by various complications, including all pathological changes involving heart and/or blood vessels. The list includes hypertension, coronary disease, failure, angina, myocardial infarction stroke. Hence, prevention early diagnosis could limit onset or progression disease. Nowadays, machine learning (ML) techniques gained a significant role in disease prediction an essential tool medicine. In this study, supervised ML-based methodology is presented through which we aim to design efficient models for CVD manifestation, highlighting SMOTE technique's superiority. Detailed analysis understanding risk factors shown explore their importance contribution prediction. These fed input features plethora ML models, trained tested identify most appropriate our objective under binary classification problem with uniform class probability distribution. Various were evaluated after use non-use Synthetic Minority Oversampling Technique (SMOTE), comparing them terms Accuracy, Recall, Precision Area Under Curve (AUC). experiment results showed that Stacking ensemble model 10-fold cross-validation prevailed over other ones achieving Accuracy 87.8%, Recall 88.3%, 88% AUC equal 98.2%.

Language: Английский

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Advancements and Prospects of Machine Learning in Medical Diagnostics: Unveiling the Future of Diagnostic Precision

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: June 26, 2024

Language: Английский

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A Comparative Analysis of Machine Learning Models: A Case Study in Predicting Chronic Kidney Disease

Sustainability, Journal Year: 2023, Volume and Issue: 15(3), P. 2754 - 2754

Published: Feb. 2, 2023

In the modern world, chronic kidney disease is one of most severe diseases that negatively affects human life. It becoming a growing problem in both developed and underdeveloped countries. An accurate timely diagnosis vital preventing treating failure. The through history has been considered unreliable many respects. To classify healthy people with disease, non-invasive methods like machine learning models are reliable efficient. our current work, we predict using different models, including logistic, probit, random forest, decision tree, k-nearest neighbor, support vector four kernel functions (linear, Laplacian, Bessel, radial basis kernels). dataset record taken as case–control study containing patients from district Buner, Khyber Pakhtunkhwa, Pakistan. compare terms classification accuracy, calculated performance measures, Brier score, sensitivity, Youdent, specificity, F1 score. Diebold Mariano test comparable prediction accuracy was also conducted to determine whether there substantial difference measures predictive models. As confirmed by results, Laplace function outperforms all other while forest competitive.

Language: Английский

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Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models

Sensors, Journal Year: 2023, Volume and Issue: 23(3), P. 1193 - 1193

Published: Jan. 20, 2023

The heart is the most vital organ of human body; thus, its improper functioning has a significant impact on life. Coronary artery disease (CAD) coronary arteries through which nourished and oxygenated. It due to formation atherosclerotic plaques wall epicardial arteries, resulting in narrowing their lumen obstruction blood flow them. can be delayed or even prevented with lifestyle changes medical intervention. Long-term risk prediction will area interest this work. In specific research paper, we experimented various machine learning (ML) models after use non-use synthetic minority oversampling technique (SMOTE), evaluating comparing them terms accuracy, precision, recall an under curve (AUC). results showed that stacking ensemble model SMOTE 10-fold cross-validation prevailed over other models, achieving accuracy 90.9 %, precision 96.7%, 87.6% AUC equal 96.1%.

Language: Английский

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Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

Computer Modeling in Engineering & Sciences, Journal Year: 2024, Volume and Issue: 139(3), P. 3513 - 3534

Published: Jan. 1, 2024

Chronic kidney disease (CKD) is a major health concern today, requiring early and accurate diagnosis.Machine learning has emerged as powerful tool for detection, medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine techniques on dataset obtained from University California, UC Irvine Machine Learning repository.The research introduces TrioNet, an ensemble model combining extreme gradient boosting, random forest, extra tree classifier, which excels in providing highly predictions CKD.Furthermore, K nearest neighbor (KNN) imputer utilized deal with missing values while synthetic minority oversampling (SMOTE) used class-imbalance problems.To ascertain efficacy proposed model, comprehensive comparative analysis conducted various models.The TrioNet KNN SMOTE outperformed other models 98.97% accuracy detecting CKD.This in-depth demonstrates model's capabilities underscores its potential valuable diagnosis CKD.

Language: Английский

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On the diagnosis of chronic kidney disease using a machine learning-based interface with explainable artificial intelligence

Intelligent Systems with Applications, Journal Year: 2024, Volume and Issue: 22, P. 200397 - 200397

Published: June 1, 2024

Chronic Kidney Disease (CKD) is increasingly recognised as a major health concern due to its rising prevalence. The average survival period without functioning kidneys typically limited approximately 18 days, creating significant need for kidney transplants and dialysis. Early detection of CKD crucial, machine learning methods have proven effective in diagnosing the condition, despite their often opaque decision-making processes. This study utilised explainable predict CKD, thereby overcoming 'black box' nature traditional predictions. Of six algorithms evaluated, extreme gradient boost (XGB) demonstrated highest accuracy. For interpretability, employed Shapley Additive Explanations (SHAP) Partial Dependency Plots (PDP), which elucidate rationale behind predictions support process. Moreover, first time, graphical user interface with explanations was developed diagnose likelihood CKD. Given critical high stakes use can aid healthcare professionals making accurate diagnoses identifying root causes.

Language: Английский

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The Significance of Machine Learning in Clinical Disease Diagnosis: A Review

International Journal of Computer Applications, Journal Year: 2023, Volume and Issue: 185(36), P. 10 - 17

Published: Oct. 25, 2023

The global need for effective disease diagnosis remains substantial, given the complexities of various mechanisms and diverse patient symptoms.To tackle these challenges, researchers, physicians, patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, develop solutions.By leveraging sophisticated ML AI methods, healthcare stakeholders gain enhanced diagnostic treatment capabilities.However, there is a scarcity research focused on algorithms enhancing accuracy computational efficiency.This investigates capacity improve transmission heart rate data in time series metrics, concentrating particularly optimizing efficiency.By exploring used applications, review presents latest trends approaches ML-based (MLBDD).The factors under consideration include algorithm utilized, types diseases targeted, employed, evaluation metrics.This aims shed light prospects healthcare, diagnosis.By analyzing current literature, study provides insights into state-of-the-art methodologies their performance metrics.

Language: Английский

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Machine learning models for chronic kidney disease diagnosis and prediction

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 87, P. 105368 - 105368

Published: Aug. 30, 2023

Language: Английский

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