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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Machine Learning Techniques for Chronic Kidney Disease Risk Prediction

Big Data and Cognitive Computing, Journal Year: 2022, Volume and Issue: 6(3), P. 98 - 98

Published: Sept. 14, 2022

Chronic kidney disease (CKD) is a condition characterized by progressive loss of function over time. It describes clinical entity that causes damage and affects the general health human body. Improper diagnosis treatment can eventually lead to end-stage renal ultimately patient’s death. Machine Learning (ML) techniques have acquired an important role in prediction are useful tool field medical science. In present research work, we aim build efficient tools for predicting CKD occurrence, following approach which exploits ML techniques. More specifically, first, apply class balancing order tackle non-uniform distribution instances two classes, then features ranking analysis performed, finally, several models trained evaluated based on various performance metrics. The derived results highlighted Rotation Forest (RotF), prevailed relation compared with Area Under Curve (AUC) 100%, Precision, Recall, F-Measure Accuracy equal 99.2%.

Language: Английский

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Artificial Intelligence of Things for Smarter Healthcare: A Survey of Advancements, Challenges, and Opportunities

IEEE Communications Surveys & Tutorials, Journal Year: 2023, Volume and Issue: 25(2), P. 1261 - 1293

Published: Jan. 1, 2023

Healthcare systems are under increasing strain due to a myriad of factors, from steadily ageing global population the current COVID-19 pandemic. In world where we have needed be connected but apart, need for enhanced remote and at-home healthcare has become clear. The Internet Things (IoT) offers promising solution. IoT created highly world, with billions devices collecting communicating data range applications, including healthcare. Due these high volumes data, natural synergy Artificial Intelligence (AI) apparent – big both enables requires AI interpret, understand, make decisions that provide optimal outcomes. this extensive survey, thoroughly explore through an examination field (AIoT) This work begins by briefly establishing unified architecture AIoT in context, sensors devices, novel communication technologies, cross-layer AI. We then examine recent research pertaining each component several key perspectives, identifying challenges, opportunities unique Several examples real-world use cases presented illustrate potential technologies. Lastly, outlines directions future

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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Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

Sensors, Journal Year: 2022, Volume and Issue: 22(14), P. 5304 - 5304

Published: July 15, 2022

Diabetes mellitus is a chronic condition characterized by disturbance in the metabolism of carbohydrates, fats and proteins. The most characteristic disorder all forms diabetes hyperglycemia, i.e., elevated blood sugar levels. modern way life has significantly increased incidence diabetes. Therefore, early diagnosis disease necessity. Machine Learning (ML) gained great popularity among healthcare providers physicians due to its high potential developing efficient tools for risk prediction, prognosis, treatment management various conditions. In this study, supervised learning methodology described that aims create prediction with efficiency type 2 occurrence. A features analysis conducted evaluate their importance explore association These are common symptoms often develop slowly diabetes, they utilized train test several ML models. Various models evaluated terms Precision, Recall, F-Measure, Accuracy AUC metrics compared under 10-fold cross-validation data splitting. Both validation methods highlighted Random Forest K-NN as best performing comparison other

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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Automated Stroke Prediction Using Machine Learning: An Explainable and Exploratory Study With a Web Application for Early Intervention

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 52288 - 52308

Published: Jan. 1, 2023

Stroke is a dangerous medical disorder that occurs when blood flow to the brain disrupted, resulting in neurological impairment. It big worldwide threat with serious health and economic implications. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention perhaps save lives. The number of people at risk growing as population ages, making precise effective systems increasingly critical. wo In comparison examination six well-known classifiers, effectiveness proposed ML technique was explored terms metrics relating both generalization capability accuracy. give insight into black-box machine learning models, we also studied two kinds explainable techniques, namely SHAP LIME, this study. (Shapley Additive Explanations) LIME (Local Interpretable Model-agnostic well-established reliable approaches explaining model decision-making, particularly industry. findings experiment revealed more complicated models outperformed simpler ones, top obtaining almost 91% accuracy other achieving 83-91% framework, includes global local methodologies, can aid standardizing gaining their enhance care treatment.

Language: Английский

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A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 25, 2024

Abstract Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current models, like the $$\text {CHA}_2\text {DS}_2\text {-VASc}$$ CHA 2 DS -VASc score, lack granularity required personalized predictions. In this study, we present nuanced and thorough stroke assessment by integrating functional insights from magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control groups, comprises eight patients. Utilizing CINE CMR, compute kinematic features, revealing smaller left atrial volumes The incorporation of displacement into our hemodynamic simulations unveils influence compliance on flow fields, emphasizing importance LA motion CFD challenging conventional rigid wall assumption hemodynamics models. Standardizing features metrics enhances differentiation cases. While standalone assessments provide limited clarity, synergistic fusion CMR-derived data patient-informed offers mechanistic understanding, distinctly segregating Specifically, investigation reveals crucial clinical insight: normalizing based ejection fraction fails to differentiate Differently, when normalized volume, clear clinically significant distinction emerges holds true both atrium its appendage, providing valuable implications settings. This work introduces novel framework seamlessly metrics, laying groundwork improved predictive highlighting significance motion-informed, assessments.

Language: Английский

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Machine Learning in Healthcare Analytics: A State-of-the-Art Review

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

Published: April 4, 2024

Language: Английский

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