Predicting Hospital Readmission Risk for Heart Failure Patients Using Machine Learning Techniques: A Comparative Study of Classification Algorithms

Published: Aug. 23, 2023

Heart failure is a frequent cause of hospitalization and readmission because the severity disease. Researchers explored using Machine Learning (ML) algorithms to forecast whether heart patients must be readmitted hospital. This study examines ML that use data from electronic health records hospital readmissions for with failure. We will assess accuracy, precision, recall, F1-score logistic regression, decision trees, random forests, Support Vector Machines (SVM), artificial neural networks. The study's results show how well predict patients' risk, which could lead personalized therapies improve patient outcomes save healthcare costs. Comparing studies in this field shows gaps model interpretability, generalizability, socioeconomic determinants prediction models.

Language: Английский

---

An Enhanced Internet of Medical Things Data Communication Based on Blockchain and Cryptography for Smart Healthcare Applications

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 305 - 313

Published: Jan. 1, 2024

Language: Английский

---

Boosting the Accuracy of Cardiovascular Disease Prediction Through SMOTE

Published: June 23, 2023

Cardiovascular Disease (CVD) affects deaths and hospitalisations. Clinical data analytics struggles to predict heart disease survival. This report compares machine learning-based cardiovascular prediction studies. The authors use a Kaggle dataset of 70,000 records 16 features show SMOTE model with hyperparameter-optimized classifiers. Random Forest outperforms KNN 13 elements in prediction. Naive Bayes SVM on complete feature sets. proposed achieves 86% accuracy, the optimised technique traditional all metrics. study analyses strengths weaknesses existing models for making predictions learning suggests promising new method.

Language: Английский

---

A Machine Learning-Based Algorithm for Early Detection of Sepsis in Hospitalized Patients: Development and Evaluation

Published: Sept. 1, 2023

Early sepsis detection improves patient outcomes and care. This research provides a Machine Learning (ML) system for hospitalized detection. Gradient boosting, an ensemble learning method, analyses data to detect early. A comprehensive electronic health record database, MIMIC-III, was used design test the algorithm. The algorithm's accuracy, precision, recall, F1 score, ROC AUC were measured. proposed approach more accurate than traditional models. It accurately predicted patients aid treatment. Real-time clinical decision-making is possible with fast prediction training. could revolutionize management by giving doctors dependable early intervention tool. algorithm must be tested in various healthcare environments demographics. To implement this technology widely, privacy ethics addressed. may improve lower costs detecting

Language: Английский

---

Gliomas Disease Prediction: An Optimized Ensemble Machine Learning-Based Approach

Published: Nov. 1, 2023

The most frequent primary brain tumors are gliomas, which call for precise prognostic models early detection and individualized care. For optimum treatment planning patient care, accurate prediction of glioma development survival is essential. improving choices outcomes in the convergence these techniques offers enormous promise. multifaceted field integrates many modalities machine-learning strategies to increase prognostication accuracy. To assist efficient tumor development, grade, prognosis, this research study provides a model that makes use machine learning algorithms KStar SMOreg. In research, voting-based approach introduced aimed at enhancing performance both feature selection phase employed glioma. This incorporates methods prediction. determine optimal scheme selected ensemble approach, various identify effective option. publicly available TCGA dataset with 24 attributes 839 instances. computational results indicate proposed method achieves 96.3% accuracy on dataset. suggested exhibits encouraging findings has great promise guiding clinical judgments outcomes.

Language: Английский

---

A Review on Explainable Artificial Intelligence for Healthcare

Published: March 7, 2025

Language: Английский

---

Explainable Artificial Intelligence (XAI) in Healthcare

Published: March 7, 2025

Language: Английский

---

A Next Generation Architecture for Internet of Things in the Automotive Supply Chain for Electric Vehicles

Published: Oct. 1, 2024

Language: Английский

---

Nano Functionalized Antenna Based IoT Enabled Devices for Health Care Applications

Published: Aug. 8, 2024

Language: Английский

---

Enhancing Human-Computer Interaction: Hand Detection for Air Writing Utilizing NumPy and OpenCV

Published: Nov. 1, 2023

In recent years, there has been a remarkable increase in interest and challenges image processing pattern recognition, specifically the context of air writing. This exciting research area significant potential to advance automation processes improve human-machine interfaces various applications. The emergence faster computers, affordable high-performance video cameras, need for automated analysis videos led an popularity object tracking, critical task computer vision. process typically encompasses detection, behavior analysis. Object tracking involves four main aspects choosing suitable representation, selecting features detecting object, object. algorithms find applications different domains, including vehicle navigation, indexing, surveillance that are automated. objective paper is create software application smart wearable devices utilizes vision track finger gestures air, functioning as motion-to-text converter air-writing. technology will facilitate communication people by enabling them generate text multiple purposes, like sending emails messages, through intermittent gestures. productive means curbs usage laptops mobiles, making it particularly beneficial individuals who deaf.

Language: Английский

---