A Pragmatic Approach of Heart and Liver Disease Prediction using Machine Learning Classifiers DOI
Karan Pal,

Sarthak Panwar,

Deepjyoti Choudhury

и другие.

Опубликована: Фев. 9, 2024

heart disease, also known as cardiovascular can cause a attack by altering the body's blood flow. Liver disease contributes to global death toll of about 2 million each year. The adaptation Artificial Intelligence and Machine Learning has latent capacity fundamentally metamorphize healthcare sector. This paper proposes undertaking comparison analysis different machine learning classifiers such Random Forest, Logistic Regression, Support Vector, Naive Bayes, Decision Tree, K-Nearest Neighbors. In our experiment, we employed four datasets, all sourced from Kaggle. Heart dataset, best accuracy achieved was 82.35%. For Disease 2020 highest 74.59%. Framingham top reached 68.6%. Lastly in liver 83.33%.

Язык: Английский

The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease DOI Creative Commons
Mohammed Andaleeb Chowdhury, Rodrigue Rizk, J. Christine Chiu

и другие.

Biomedicines, Год журнала: 2025, Номер 13(2), С. 427 - 427

Опубликована: Фев. 10, 2025

The application of artificial intelligence (AI) and machine learning (ML) in medicine healthcare has been extensively explored across various areas. AI ML can revolutionize cardiovascular disease management by significantly enhancing diagnostic accuracy, prediction, workflow optimization, resource utilization. This review summarizes current advancements concerning disease, including their clinical investigation use primary cardiac imaging techniques, common categories, research, patient care, outcome prediction. We analyze discuss commonly used models, algorithms, methodologies, highlighting roles improving outcomes while addressing limitations future applications. Furthermore, this emphasizes the transformative potential practice decision making, reducing human error, monitoring support, creating more efficient workflows for complex conditions.

Язык: Английский

Процитировано

0
A Novel Approach for Performance Evaluation and Effectiveness of Data-Driven Heart Disease Diagnosis DOI
Md Aminul Islam, Anindya Nag,

Ayontika Das

и другие.

Lecture notes in electrical engineering, Год журнала: 2025, Номер unknown, С. 231 - 243

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
The application of machine learning models in a resource-constrained environment DOI

Amelia Jiménez Heffernan,

Jaewook Shin,

Kemunto Otoki

и другие.

Irish Journal of Medical Science (1971 -), Год журнала: 2025, Номер unknown

Опубликована: Апрель 2, 2025

Язык: Английский

Процитировано

0
Advancing Health Diagnostics: AI-Powered CVD-REF Framework for Precise and Early Risk Assessment DOI

Vishnu Priyan S,

N. Vijayalakshmi,

Gulivindala Suresh

и другие.

Journal of Machine and Computing, Год журнала: 2025, Номер unknown, С. 1248 - 1264

Опубликована: Апрель 5, 2025

Deprivation of Critical Care systems are a major cause fatality worldwide, highlighting it’s need for saving human lives. This study proposes novel hybrid ensemble model, which integrates Random Forests, Gradient Boosting Machines (GBM), and Neural Networks to enhance the predictive accuracy diagnostics. The methodology combines data pre-processing, feature selection, learning, ensuring robust reliable predictions. Comprehensive pre-processing includes K-Nearest Neighbours (KNN) imputation missing values, Z-Score normalization scaling, Polynomial Feature Generation non-linear interactions. selection performed using Recursive Elimination (RFE) Mutual Information relevant variable retention. proposed model produces 98.55% accuracy, very surpassing nine baseline models, that XGBoost, Networks. Additional metrics such as precision (97.80%), recall (98.12%), F1-Score (98.00%), ROC-AUC (99.12%) further validate model's robustness. framework not only demonstrates superior but also ensures computational efficiency, making it viable deployment in real-world healthcare settings.

Язык: Английский

Процитировано

0
A novel hybrid CNN-KNN ensemble voting classifier for Parkinson’s disease prediction from hand sketching images DOI
Shawki Saleh, Asmae Ouhmida, Bouchaib Cherradi

и другие.

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Май 14, 2024

Язык: Английский

Процитировано

3
Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions (Preprint) DOI Creative Commons
Yuqing Cai, Da-Xin Gong,

Li-Ying Tang

и другие.

Journal of Medical Internet Research, Год журнала: 2024, Номер 26, С. e47645 - e47645

Опубликована: Июнь 13, 2024

In recent years, there has been explosive development in artificial intelligence (AI), which widely applied the health care field. As a typical AI technology, machine learning models have emerged with great potential predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, are expected to play crucial role reducing incidence mortality rates diseases. Although field become research hot spot, still many pitfalls that researchers need pay close attention to. These may affect predictive performance, credibility, reliability, reproducibility studied models, ultimately value affecting prospects clinical application. Therefore, identifying avoiding these is task before implementing research. However, currently lack comprehensive summary on this topic. This viewpoint aims analyze existing problems terms quality, set characteristics, model design, statistical methods, as well implications, provide possible solutions problems, such gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using specific algorithms address targeted issues, standardizing outcomes evaluation criteria, enhancing fairness replicability, goal offering reference assistance researchers, algorithm developers, policy makers, practitioners.

Язык: Английский

Процитировано

3
A Comprehensive Review of Machine Learning Algorithms and Its Application in Groundwater Quality Prediction DOI

Harsh Pandya,

Khushi Jaiswal,

Manan Shah

и другие.

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Июнь 24, 2024

Язык: Английский

Процитировано

3
An objective cross-sectional assessment of ChatGPT in hematology-oncology manuscript composition: Balancing promise with factual inaccuracies DOI Creative Commons
Suvir Singh, Pooja Prasad,

Kaveri Joshi

и другие.

Cancer Research Statistics and Treatment, Год журнала: 2024, Номер 7(2), С. 206 - 215

Опубликована: Апрель 1, 2024

ABSTRACT Background: Artificial intelligence (AI)-based large language models (LLMs), such as Chat Generative Pre-training Transformer (ChatGPT), exhibit promise in aiding manuscript composition and literature search, encompassing various research tasks. However, their utilization remains unregulated. Objectives: The primary objective of this study was to objectively assess the ability ChatGPT 3.5 (free version) assist with tasks associated preparation based on pre-defined scoring criteria. Secondary objectives included an assessment factual accuracy data any false information returned by ChatGPT. Materials Methods: This cross-sectional planned Departments Clinical Hematology Medical Oncology Dayanand College Hospital, Ludhiana, Punjab, India, a tertiary care referral center. Between July 1, 2023, 30, seven prompts comprising queries related design, specific data, or complex discussion hematology/oncology subjects were used. responses scored detailed criteria for completeness, independently performed panel five reviewers current expertise field hematology/medical oncology. Negative marking inaccuracies. Cronbach’s alpha interclass correlation coefficient calculated inter-observer agreement. Results: readily provided structural components customize immediately. presence inaccuracies, fictional citations, presented confidently notable drawbacks. 0.995, intraclass indicating good overall score 34.2 out 90, poor veracity references. Conclusion: iteration rapidly provides plausible professional-looking up-to-date topics but is hindered significant Future focusing improving response addressing ethical considerations content generated LLMs will help us maximize potential scientific paper development.

Язык: Английский

Процитировано

3
Predicting the Occurrence of Metabolic Syndrome Using Machine Learning Models DOI Creative Commons
Μαρία Τρίγκα, Ηλίας Δρίτσας

Computation, Год журнала: 2023, Номер 11(9), С. 170 - 170

Опубликована: Сен. 3, 2023

The term metabolic syndrome describes the clinical coexistence of pathological disorders that can lead to development cardiovascular disease and diabetes in long term, which is why it now considered an initial stage above entities. Metabolic (MetSyn) closely associated with increased body weight, obesity, a sedentary lifestyle. necessity prevention early diagnosis imperative. In this research article, we experiment various supervised machine learning (ML) models predict risk developing MetSyn. addition, predictive ability accuracy using synthetic minority oversampling technique (SMOTE) are illustrated. evaluation ML highlights superiority stacking ensemble algorithm compared other algorithms, achieving 89.35%; precision, recall, F1 score values 0.898; area under curve (AUC) value 0.965 SMOTE 10-fold cross-validation.

Язык: Английский

Процитировано

8
Chaos in Physiological Control Systems: Health or Disease? DOI Creative Commons
Olfa Boubaker

Chaos Theory and Applications, Год журнала: 2024, Номер 6(1), С. 1 - 12

Опубликована: Март 5, 2024

During the nineties, Rössler’s have reported in their famous book “Chaos Physiology,” that “physiology is mother of Chaos.” Moreover, several researchers proved Chaos a generic characteristic systems physiology. In context disease, like for example growth cancer cell populations, often refers to irregular and unpredictable patterns. such cases, signatures can be used prove existence some pathologies. However, other physiological behaviors, form order disguised as disorder signature healthy functions. This case human brain behavior. As boundary between health disease not always clear-cut chaotic physiology, conditions may involve transitions ordered states. Understanding these identifying critical points crucial predicting Healthy vs. pathological Chaos. Using recent advances dynamics, this survey paper tries answer question: when sign or disease?

Язык: Английский

Процитировано

2