Machine Learning-Based Approaches for the Prognosis and Prediction of Multiple Diseases

Published: Jan. 24, 2024

The rapid progress in machine learning techniques has significantly transformed healthcare which enables the simultaneous and accurate detection of multiple diseases. This paper delves into application diverse algorithms for multi-disease by using a comprehensive dataset focuses on three diseases i.e. diabetes, gonorrhoea, typhoid. been meticulously pre-processed graphically visualized to discern patterns represent against emotional states/urges critical feelings. Subsequently, range classifiers includes logistic regression, Adaboost, random forest, support vector machine, CatBoost, Light Gradient Boosting Classifier, Naïve Bayes, XGBoost, KNN, Decision Tree, are trained this dataset. Their performance across these different classes is rigorously evaluated various parameters such as accuracy, F1 score, recall, precision. During execution, Adaboost emerged top performer, achieving an impressive accuracy 94.37% maintaining precision, score 0.94, indicates its robustness detection.

Language: Английский

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AI Algorithms for Modeling the Risk, Progression, and Treatment of Sepsis, Including Early-Onset Sepsis—A Systematic Review

Journal of Clinical Medicine, Journal Year: 2024, Volume and Issue: 13(19), P. 5959 - 5959

Published: Oct. 7, 2024

Sepsis remains a significant contributor to neonatal mortality worldwide. However, the nonspecific nature of sepsis symptoms in neonates often leads necessity empirical treatment, placing burden ineffective treatment on patients. Furthermore, global challenge antimicrobial resistance is exacerbating situation. Artificial intelligence (AI) transforming medical practice and hospital settings. AI shows great potential for assessing risk devising optimal strategies.

Language: Английский

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A Comprehensive Analysis of Hypertension Disease Risk-Factors, Diagnostics, and Detections Using Deep Learning-Based Approaches

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(4), P. 1939 - 1958

Published: Dec. 14, 2023

Language: Английский

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Machine Learning based Approaches for Accurately Diagnosis and Detection of Hypertension Disease

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 169 - 173

Published: Dec. 1, 2023

Predicting hypertension accurately is essential for early intervention and effective disease management. In recent years, machine learning techniques have attracted considerable interest their potential to predict diagnose a variety of medical conditions, including hypertension. The purpose this article provide an insight into how models are used hypertension, emphasizing the methodologies employed, performance metrics, difficulties encountered. article, properly analyze disease, symptoms investigations taken consideration pre-process features. After pre-processing, feature scaling applied optimize prediction results. Further, learning-based classify determine whether person has issues or not. Based on our analysis, we concluded that random forest KNN detect

Language: Английский

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American Journal of Biomedical Science & Research, Journal Year: 2024, Volume and Issue: 22(3)

Published: May 8, 2024

In fetal medicine, artificial intelligence plays a crucial role in preventing congenital abnormalities.Anomalies of heart and brain ultrasonography MRI have been shown to be recognizable, detectable, localizable by ML algorithms CNNs.Artificial Intelligence (AI) systems are capable carrying out intricate analyses aberrant image patterns order categorize predict malformations fetuses.The Artificial the prediction risk stratification anomalies is explored this narrative review.Fetal imaging (ultrasonography MRI) examination may optimized DL reduce time, lighten doctor's workload, increase diagnostic precision for anomalies.The current study's objective evaluate being utilized automate screening anomalies.It also compares terms efficiency quality anomaly detection fetus.The review highlights importance integrating multiple data sources, analyzing longitudinal data, creating larger, more varied datasets predicting significance human clinical expertise, interpretability, prospective validation real-world settings emphasized.

Language: Английский

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Machine Learning Approaches in Virtual Biopsy: A Review of Recent Developments and Applications

Published: Oct. 24, 2024

Language: Английский

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