Research Article
Death Events from Heart Failure Prediction Using Machine Learning Approach
Hosea Isaac Gungbias
,
Mulapnen Haruna Kassem*
Issue:
Volume 11, Issue 1, March 2025
Pages:
1-10
Received:
19 February 2025
Accepted:
27 February 2025
Published:
11 March 2025
DOI:
10.11648/j.ijdst.20251101.11
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Views:
Abstract: Heart failure is a significant global health concern, contributing to high mortality rates and imposing substantial burdens on healthcare systems. Early prediction of mortality in heart failure patients can facilitate timely interventions, enhance patient management, and improve overall survival outcomes. This study applies machine learning techniques to predict death events among heart failure patients using clinical data. Five classification algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbor (KNN), and Gaussian Naïve Bayes—are implemented on a dataset containing 5,000 patient records with 13 clinical attributes obtained from Kaggle. The research methodology includes extensive data preprocessing, such as missing value imputation using mean/mode strategies, standardization, feature selection via ANOVA P-value testing, and data balancing with the Synthetic Minority Over-sampling Technique (SMOTE). Model optimization was performed through hyperparameter tuning and cross-validation to enhance predictive accuracy. The results from two experimental settings—one without optimization and one with hyperparameter tuning, feature selection, and Principal Component Analysis (PCA)—show that K-Nearest Neighbor achieved the highest accuracy (98.5%) and precision (98.9%) after optimization. In contrast, Random Forest performed exceptionally well without tuning, achieving an accuracy of 99.2% and an F1-score of 98.7%. The findings demonstrate the effectiveness of machine learning in heart failure prognosis, providing valuable insights for clinical decision-making and personalized patient care.
Abstract: Heart failure is a significant global health concern, contributing to high mortality rates and imposing substantial burdens on healthcare systems. Early prediction of mortality in heart failure patients can facilitate timely interventions, enhance patient management, and improve overall survival outcomes. This study applies machine learning techniq...
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Review Article
The Importance Foresight Internet of Things, Virtual Reality and Telemedicine in Health Care
Issue:
Volume 11, Issue 1, March 2025
Pages:
11-17
Received:
12 January 2025
Accepted:
22 April 2025
Published:
29 May 2025
DOI:
10.11648/j.ijdst.20251101.12
Downloads:
Views:
Abstract: Background: Future studies are the science of shaping the future, in a conscious, scientific, and future-thinking way that protects people from surprises from a storm of dramatic changes and developments. Foresight is a systematic way of looking at the long-term future and making appropriate decisions. This knowledge is a type of study method that strives to unite and integrate the talents and abilities of individuals to create a desirable and ideal future for themselves. Researchers with different research methods emphasize the study of various technological solutions to increase the quantity and quality of healthcare delivery. We need deeper research to get a good understanding of these technologies. The development of health technologies can improve health levels, living standards, and economic growth, and some of these technologies such as telemedicine, the Internet of things, the 3D virtual world, etc. Have reduced medical costs and medical malpractice. Nevertheless, new technologies related to diagnosis, prevention, and rehabilitation will lead to changes in the methods of healthcare delivery. So various technologies create new and different demands and due to widespread changes in health technologies, foresight, and future thinking in health technologies are of great importance. Objective: These studies provide valuable insights into the future trends and potential impacts of emerging technologies. This information helps healthcare providers and policymakers make more informed strategic decisions about technology investments and implementation. Also helps healthcare organizations identify and mitigate potential risks associated with the adoption of new technologies, ensuring a smoother and more successful implementation process. this study aims to highlight the importance of digital health development (telemedicine, Internet of Things, 3D virtual world) and their foresight. Main Ideas: In summary, healthcare technology development is crucial for strategic planning, efficient resource allocation, improved patient care, and fostering innovation, all of which contribute to the transformation of healthcare delivery. Conclusion: Therefore, a special focus on foresight and the construction of new digital technologies in health to better manage time, and resources, and increase speed and accuracy in health services is essential. So, engineers and medical science professionals need to take a forward-thinking view and pay attention to uncertainties and spaces full of changes and the prediction of future demand in healthcare organizations is an essential element of the planning process.
Abstract: Background: Future studies are the science of shaping the future, in a conscious, scientific, and future-thinking way that protects people from surprises from a storm of dramatic changes and developments. Foresight is a systematic way of looking at the long-term future and making appropriate decisions. This knowledge is a type of study method that ...
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