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

Sarthak Panwar,

Deepjyoti Choudhury

et al.

Published: Feb. 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%.

Language: Английский

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

et al.

Biomedicines, Journal Year: 2025, Volume and Issue: 13(2), P. 427 - 427

Published: Feb. 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.

Language: Английский

Citations

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

Ayontika Das

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 231 - 243

Published: Jan. 1, 2025

Language: Английский

Citations

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

Amelia Jiménez Heffernan,

Jaewook Shin,

Kemunto Otoki

et al.

Irish Journal of Medical Science (1971 -), Journal Year: 2025, Volume and Issue: unknown

Published: April 2, 2025

Language: Английский

Citations

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

Vishnu Priyan S,

N. Vijayalakshmi,

Gulivindala Suresh

et al.

Journal of Machine and Computing, Journal Year: 2025, Volume and Issue: unknown, P. 1248 - 1264

Published: April 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.

Language: Английский

Citations

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

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: May 14, 2024

Language: Английский

Citations

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

et al.

Journal of Medical Internet Research, Journal Year: 2024, Volume and Issue: 26, P. e47645 - e47645

Published: June 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.

Language: Английский

Citations

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

Harsh Pandya,

Khushi Jaiswal,

Manan Shah

et al.

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

Published: June 24, 2024

Language: Английский

Citations

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

et al.

Cancer Research Statistics and Treatment, Journal Year: 2024, Volume and Issue: 7(2), P. 206 - 215

Published: April 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.

Language: Английский

Citations

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

Computation, Journal Year: 2023, Volume and Issue: 11(9), P. 170 - 170

Published: Sept. 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.

Language: Английский

Citations

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

Chaos Theory and Applications, Journal Year: 2024, Volume and Issue: 6(1), P. 1 - 12

Published: March 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?

Language: Английский

Citations

2