Heart Disease Prediction using Machine Learning

Kasabe, Riddhi; Narang, Geetika

doi:10.17577/ijertv9is080128

Cited by 12 publications

(3 citation statements)

References 5 publications

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“…In this work author claimed that Naive Bayes and Random Forest are able to identify the detail insight of data compare to other techniques. A detail description of the classification algorithm was represented in the paper in (9) . S. Shylaja et al in (10) created a hybrid model for heart disease prediction where SVM and ANN were combined.…”

Section: Literature Reviewmentioning

confidence: 99%

An optimized Model for Heart Disease Prediction with Customized Ensemble Voting Classifier and Nature Inspired Optimization

Majumder,

Gupta,

Singh

et al. 2023

IJST

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Objective:To develop an optimized model for prediction of heart disease. Methods/findings: The model has been built applying Customized Ensemble Voting Classifier where the weights of each base classifier have been calculated considering accuracy, specificity and sensitivity. For feature selection Chi Square method has been applied. The performance of the model has been enhanced using Firefly algorithm. The proposed model has achieved 85.52% accuracy. Novelty: The novelty of our research paper on heart disease prediction lies in the integration of machine learning algorithms to develop an accurate predictive model. Multiple classifiers have grouped together through a customized ensemble voting classifier. For assigning the weight not only accuracy is considered but also specificity and sensitivity scores have also been considered and then performance has been optimized with Firefly algorithm which makes the method comprehensive and reliable for predicting heart disease risk and could ultimately lead to more effective prevention and treatment strategies.

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Section: Literature Reviewmentioning

confidence: 99%

An optimized Model for Heart Disease Prediction with Customized Ensemble Voting Classifier and Nature Inspired Optimization

Majumder,

Gupta,

Singh

et al. 2023

IJST

View full text Add to dashboard Cite

show abstract

“…Pooja Anbuselvan. [2] Aimed to identify the most accurate algorithm for predicting the development of heart disease by analyzing various classification techniques. The study used pre-processed data and techniques like Logistic Regression, Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Random Forest and XGBoost.…”

Section: IImentioning

confidence: 99%

Machine Learning for Personalized Risk Assessment and Early Detection of Heart Disease

Anilkumar¹,

Sankar²

2023

IJRASET

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Today in our busy life it very difficult for us to look after our health. Cardio vascular diseases are very common now days which cause loss of billions of lives world-wide. Our lifestyles have a major impact on our health causing various chronic diseases. Machine learning (ML) can revolutionize the field of cardiovascular disease prediction by providing more accurate, nuanced, and personalized risk assessment, leading to improved patient health. A properly trained machine learning model can easily detect or predict the heart disease. Within the context of using artificial intelligence for forecasting and assessing the occurrence of heart ailments, here are various factors that contribute to the heart disease to be considered, such as lifestyle choices and medical conditions to predict whether the person is at high risk or not. ML technology can also analyze large datasets of patient data to identify patterns and risk factors that cannot be easily distinguished, predicted, or detected using traditional methods. Estimators like k-NN classifier, Decision tree classifier, Gradient boosting classifier and Gaussian Naive bayes (NB) classifier, are provided with different characteristics or feature extracted from the dataset, demonstrated consistent and reliable performance in predicting heart disease. The utilization of these attributes can potentially aid the medical field for on time detection and diagnosis of the heart disease.

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“…The heart is a crucial organ in the human body, and its malfunctioning can adversely impact other vital organs such as the brain and kidneys. The heart serves as a pump, circulating blood throughout the body, and cessation of its beating can result in death within minutes [1]. Figure 1 shows an image of a person with a normal heart and an image of a person with an enlarged heart.…”

Section: Introductionmentioning

confidence: 99%

Survey On Techniques For Cardiomegaly Prediction By Chest X-ray Images

Ahmed,

Al Saadi

2023

Jour. Kufa Math. Comp.

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Cardiomegaly is a condition characterized by an enlarged heart, which can be indicative of various underlying health issues. Early diagnosis of this condition lessens the patient's repercussions. The paper gives a thorough overview of the disease, its classifications, algorithms, and methods employed emphasizing the challenges encountered in this field. Traditional methods of diagnosing cardiomegaly rely on medical imaging techniques such as echocardiography or chest X-rays, which can be time-consuming and require specialized expertise to interpret. However, recent advances in deep learning algorithms have shown promise in accurately identifying cardiomegaly and its underlying causes from medical images. The use of deep learning algorithms in the diagnosis of cardiomegaly has the potential to improve both the speed and accuracy of diagnosis, leading to better patient outcomes and more efficient use of healthcare resources. Moreover, deep learning algorithms can potentially identify subtle changes in heart size over time, allowing for earlier detection and treatment of cardiomegaly.

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Heart Disease Prediction using Machine Learning

Cited by 12 publications

References 5 publications

An optimized Model for Heart Disease Prediction with Customized Ensemble Voting Classifier and Nature Inspired Optimization

An optimized Model for Heart Disease Prediction with Customized Ensemble Voting Classifier and Nature Inspired Optimization

Machine Learning for Personalized Risk Assessment and Early Detection of Heart Disease

Survey On Techniques For Cardiomegaly Prediction By Chest X-ray Images

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