Cardiovascular Disease Prediction Using Machine Learning Models

Nikam, Atharv; Bhandari, Sanket; Mhaske, Aditya; Mantri, Shamla

doi:10.1109/punecon50868.2020.9362367

Cited by 45 publications

(16 citation statements)

References 12 publications

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“…Shorewall et al [10] employed a stacking model for the identification of CVDs, and they obtained 75.1% classification accuracy. Atharv Nikam et al [11] developed a ML-based model to diagnosis CVDs, and they achieved 73.13% accuracy on DT learning approach.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Approach for the Prediction of Cardiovascular Diseases

Kamireddy,

Darapureddy

2023

The 4th International Electronic Conference on Applied Sciences

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Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Approach for the Prediction of Cardiovascular Diseases

Kamireddy,

Darapureddy

2023

The 4th International Electronic Conference on Applied Sciences

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“…To improve prediction accuracy and explore events related to heart disease, a predictive analytic framework was designed using a Random Forest classifier, and experimental results showed that classification using the Classification algorithm can be successfully used in predicting events and risk factors related to HD [6,12]. To identify cardiac sickness a decision Tree employs clustering methods (K-Means) [13]. The inlier approach involving clusters produced a classification accuracy of 83.9 % within the diagnosis of heart disease.…”

Section: Journal Of Computing and Biomedical Informaticsmentioning

confidence: 99%

“…Where 'A' and 'B' is the event and P(B) is not equal to zero The Light Gradient Boosting Machine approach divides its tree leaf-wise with the greatest fit adopting decision tree approaches, while other boosting techniques split its tree depth-wise or level-wise rather than leafwise. As a result, the Light GBM algorithm achieves better accuracy, which is difficult to achieve with any of the current boosting approaches [13]. We're utilizing and fine-tuning the model parameters using cross-validation of the LGBM Classifier model.…”

Section: Naive Bayesmentioning

confidence: 99%

Exploiting Machine Learning Models for Identification of Heart Diseases

Ullah

Imran

Ashfaq

et al. 2022

JCBI

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Cardiovascular illnesses have surpassed disease as the leading cause of death in industrialized, emerging, and underprivileged countries in recent decades. The mortality rate can be lowered through early detection and effective care of cardiac diseases. However, reliable detection of heart disorders in all conditions and serving doctors with 24-hour medical consultations are not possible since they require more intelligence, time, and talent to train the computer for a specific duty. Artificial intelligence and machine learning algorithms may have a substantial impact on the lives of those who are afflicted with chronic diseases. In this work, we used artificial intelligence to create a hybrid model for the prediction of cardiac disease and flags for prevention. The goal of this research was to develop a prediction model that can identify and characterize a patient's illness. We used data of 68975 patients from the University of California, Irvine's repository and trained several algorithms for CVD illness prediction. In our simulation maximum classifiers have achieved the greatest accuracy, which is a huge accomplishment for our research team. Our proposed hybrid ML algorithm framework improved CVD prediction performance when compared to previously offered prediction models. The proposed methods are decision tree and random forest models, which have a 99.98% accuracy for training, followed by support vector machine, which has a 99.30% accuracy followed by other classifiers which yield higher results in comparison. Pre-existing CVD history was considered the most important factor determining the prediction model's accuracy. Our findings reveal that our CVD prediction model based on machine learning techniques developed for health screening datasets is simple to apply and more accurate. Our proposed classifiers have the best accuracy rate.

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“…This work presents Huang-started k-mode clustering to increase classification accuracy. Nikam et al [35] suggested a model that would use methods from ML to predict cardiovascular illness based on characteristics. One of the aspects that stands out most is the body mass index (BMI).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

Nrusimhadri,

Swain,

Rao

et al. 2024

IJPHS

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Machine learning (ML) improves operations in many industries, including medicine. It affects the prognosis of several disorders, including heart disease. If predicted, it may provide doctors with new insights and allow them to treat each patient individually. If anticipated, it may provide medical practitioners with valuable information. Our team uses machine learning algorithms to study heart disease risk. This research will compare decision trees, AdaBoost, support vector machines, artificial neural networks (ANN), and customized ANN. The study will include this analysis. The given model will leverage the dataset of general information and medical test results. Our model uses particle swarm optimization (PSO) and k-nearest neighbors (KNN). Algorithm for feature selection. The model reduces dimensionality using evolutionary algorithms and neural networks. We compared the numerous assessment criteria to the current models, our model, and earlier models. Because of this, the suggested model's suitability was rated with the highest accuracy.

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Cardiovascular Disease Prediction Using Machine Learning Models

Cited by 45 publications

References 12 publications

A Machine Learning-Based Approach for the Prediction of Cardiovascular Diseases

A Machine Learning-Based Approach for the Prediction of Cardiovascular Diseases

Exploiting Machine Learning Models for Identification of Heart Diseases

Evaluation of cardiovascular disease in diabetic patients using machine learning techniques

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