Predicting pregnancy-related pelvic girdle pain using machine learning

Musculoskeletal Science and Practice, Journal Year: 2025, Volume and Issue: unknown, P. 103321 - 103321

Published: March 1, 2025

Language: Английский

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ML Based Interactive Disease Prediction Model

2022 IEEE Delhi Section Conference (DELCON), Journal Year: 2022, Volume and Issue: unknown, P. 1 - 5

Published: Feb. 11, 2022

The application of Machine learning algorithms to predict diseases is one the finest methodology reduce heavy work load on doctors and related medical staff. Based World Health Organization (WHO) report, about 85% heart disease deaths are due Heart Attacks Strokes. In India average death rate cardiovascular 272 per 10,000 population which greater than global 235 population. From recent survey results, was released by Union Ministry Family Welfare (MoFHW), Diabetes positive ratio gradually increasing in India. 11.5 percent people were tested for among urban rural Indians who with age 45 above. Even there availability wide range treatment methods stroke patients & diabetes, attack major cause all parts areas entire There several factors causing diabetes problems include Age, Gender, Blood Pressure, Glucose levels, Skin thickness Insulin. These easily measured primary care facility centres. accurate estimation analysis reports data may help predicting future including diabetes. Globally, computerized machine trend now. Monitoring Departments Fields uses analyse a wider way solve fraction seconds. famous proverb "Prevention Better Than Cure", if we apply this medico health field can save from Diseases (HD's) along Diabetes. proposed Dual prediction technique user interactive based method. method observe inputs end realistic disease. presented work, used Logistic regression model (LR) Support vector (SVM) diseases. works 85 78 accuracy respectively.

Language: Английский

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RETRACTED ARTICLE: Learning-based techniques for heart disease prediction: a survey of models and performance metrics

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(13), P. 39867 - 39921

Published: Oct. 6, 2023

Language: Английский

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Healthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A Survey

Research Square (Research Square), Journal Year: 2022, Volume and Issue: unknown

Published: July 28, 2022

Abstract Aim This paper aims to present a comprehensive survey of existing machine learning and deep approaches utilized in healthcare prediction, as well identify inherent obstacles applying these the prediction domain. Background Healthcare has been significant factor saving human lives recent years. In domain healthcare, there is rapid development intelligent systems for analyzing complicated relationships among data transforming them into real information use process. Consequently, artificial intelligence rapidly industry. Thus comes role depending on creation steps that diagnose predict diseases, whether from clinical or based images, provide tremendous support by simulating perception can even diseases are difficult detect intelligence. Methods The studies discussed this have presented journals published IEEE, Springer, Elsevier. Machine learning, surgery, cardiology, radiology, hepatology, nephrology some terms used search studies. chosen concerned with algorithms prediction. Results A total 40 working papers were selected methodology each was clarified. Conclusion presents current challenges shown plays diagnosing.

Language: Английский

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Machine Learning based Prediction and Diagnosis of Heart Disease using multiple models

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: March 7, 2023

Abstract Nowadays, heart disease is considered to be the main cause of sickness. Since majority people are unaware their own kind and severity disease, now a significant problem that affects all ages. On other hand, manual approach prediction challenging often requires capability choose relevant approach. To resolve these issues, various machine-learning models playing vital role in automatic medical field. In this study, we have calculated made comparison accuracy machine learning such as SVM, KNN, Logistic Regression, Decision Tree, Random Forest, Gaussian Naive Bayes, AdaBoost, Extra Tree Classifier Gradient Boosting for using UCI repository dataset training testing models. Among used, highest 95.08% obtained by model The major aim paper get reliable, computationally effective algorithm prediction.

Language: Английский

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Analyzing the efficiency of heart disease prediction using SVM and an innovative penalty based logistic regression classifier (IPLR)

AIP conference proceedings, Journal Year: 2024, Volume and Issue: 2729, P. 060007 - 060007

Published: Jan. 1, 2024

The purpose of the work is to assess accuracy and precision in predicting heart disease by Support Vector Machine (SVM) Logistic Regression Classification algorithms. Materials Methods: appealed on a dataset which consists SVM classifiers has been proposed developed. sample size was calculated as 55 each group using G power. Sample clinical enrolment ratio 1. evaluated recorded. Results: classifier produces 53.04% data set used whereas predicts same at rate 84.7%. significant value 0.0. Hence LR better than SVM. Conclusion: performance compared with terms both accuracy.

Language: Английский

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Heart Disease Prediction using CNN, Deep Learning Model

International Journal for Research in Applied Science and Engineering Technology, Journal Year: 2020, Volume and Issue: 8(12), P. 875 - 881

Published: Dec. 31, 2020

Heart disease is one of the most serious health threat growing among worldwide, for which mortality rate around world very high. Early detection heart could save many lives, accurate crucial care persons through regular clinical data and its analysis. Artificial intelligence effective solution decision making predictions. Medical industry showing enormous development in using information technology, artificial play major role. In proposed work, deep learning based approach on done Cleveland dataset. However existing studies are handled Machine technique. The work detects Convolutional Neural Networks. Experimental results shows our achieves high level accuracy prediction disease.

Language: Английский

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Logistic Regression Models in Predicting Heart Disease

Journal of Physics Conference Series, Journal Year: 2021, Volume and Issue: 1769(1), P. 012024 - 012024

Published: Jan. 1, 2021

Abstract This paper predicts the risk of suffering from heart disease among elderly by exploring feasibility using logistic regression models. Through technology data mining, main pathogenic factors were found, and incidence was predicted model. The accuracy model compared with other explored algorithms, I found that worthy research in field prediction.

Language: Английский

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Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Journal of Computer Electronic and Telecommunication, Journal Year: 2024, Volume and Issue: 4(2)

Published: Jan. 26, 2024

This study addresses the problem of heart disease detection, a critical concern in public health. The research aims to compare performance Convolutional Neural Networks (CNN) with conventional machine learning algorithms diagnosing using dataset comprising 14 features. primary objective is determine whether CNNs can provide more accurate and reliable results than traditional techniques. employs rigorous preprocessing, normalizing relevant features, splits into an 80-20 training-testing split. model trained for 300 epochs batch size 64, evaluation conducted confusion matrices classification reports. reveal that CNN achieved remarkable accuracy 100%, demonstrating its potential outperform algorithms. These findings emphasize significance deep techniques improving diagnostics, although further needed optimize models address interpretability concerns practical implementation healthcare settings.

Language: Английский

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Smart Healthcare Based on 6G Network Using Wireless Communication and Machine Learning Model in Cardiac Disease Analysis

Wireless Personal Communications, Journal Year: 2024, Volume and Issue: unknown

Published: May 15, 2024

Language: Английский

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