Heart Disease Prediction Using Machine Learning Technique

Pattnaik, Priyanka; Padhy, Susanta Kumar; Mishra, Bhabani Shankar Prasad; Mishra, Subhashree; Mallick, Pradeep Kumar

doi:10.1007/978-981-15-8752-8_20

Cited by 3 publications

(6 citation statements)

References 8 publications

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“…Prerequisite is the Bayes Theorem with the assumption -independent nature of the attributes among the same class [12]. It is referred to as a distribution methodology that includes an expectation of predictors and independence [10].…”

Section: Naïve Bayesmentioning

confidence: 99%

“…They are used for classification where each path is a set of decisions leading to one class. Root to the leaf node represents a set of rules, which leads to a classification [10]. It is a flowchart: branching node is a test on a feature; branch indicates the inferences made from the tests and the leaf node providing information about a single class label.…”

Section: Decision Tree Classifiermentioning

confidence: 99%

“…In Figure 5 dataset is visualized using Decision Tree, with maximum depth as 4 and minimum sample leaf as 7.  Point in the one-dimensional space,  Line in two-dimensional space,  Plane in the three-dimensional space, and  Hyperplane in the four-dimensional space [12] 4.5 K -Nearest Neighbour It is the convenient, easy to control and supervised ML classifier availed by following the steps below [10].…”

Section: Decision Tree Classifiermentioning

confidence: 99%

“…vii. Return median (k labels) on relapse; return mode (k labels) on classification [10]. This is to be noted that k-values should never be considered as 1.…”

Section: Decision Tree Classifiermentioning

confidence: 99%

“…iv. Construct the forest using replication of steps for n number of times for 'n' decision trees [10].…”

Section: Random Forestmentioning

confidence: 99%

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Cardiac Disease Prediction using Supervised Machine Learning Techniques.

Gupta

Saha

Reddy

et al. 2022

J. Phys.: Conf. Ser.

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Diagnosis of cardiac disease requires being more accurate, precise, and reliable. The number of death cases due to cardiac attacks is increasing exponentially day by day. Thus, practical approaches for earlier diagnosis of cardiac or heart disease are done to achieve prompt management of the disease. Various supervised machine learning techniques like K-Nearest Neighbour, Decision Tree, Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) model are used for predicting cardiac disease using a dataset that was collected from the repository of the University of California, Irvine (UCI). The results depict that Logistic Regression was better than all other supervised classifiers in terms of the performance metrics. The model is also less risky since the number of false negatives is low as compared to other models as per the confusion matrix of all the models. In addition, ensemble techniques can be approached for the accuracy improvement of the classifier. Jupyter notebook is the best tool, for the implementation of Python Programming having many types of libraries, header files, for accurate and precise work.

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Section: Naïve Bayesmentioning

confidence: 99%

Section: Decision Tree Classifiermentioning

confidence: 99%

Section: Decision Tree Classifiermentioning

confidence: 99%

“…vii. Return median (k labels) on relapse; return mode (k labels) on classification [10]. This is to be noted that k-values should never be considered as 1.…”

Section: Decision Tree Classifiermentioning

confidence: 99%

“…iv. Construct the forest using replication of steps for n number of times for 'n' decision trees [10].…”

Section: Random Forestmentioning

confidence: 99%

See 3 more Smart Citations

Cardiac Disease Prediction using Supervised Machine Learning Techniques.

Gupta

Saha

Reddy

et al. 2022

J. Phys.: Conf. Ser.

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Application of Machine Learning Algorithm: Forecasting Heart disease

Lohia,

Pawar

2024

2024 3rd International Conference for Innovation in Technology (INOCON)

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Early Detection of Heart Disease Using Machine Learning Approach

Zabeeulla¹,

Sharma²,

Anand³

2023

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Heart attack is one of the leading causes of morbidity in the worldwide population. Cardiovascular disease is one of the major diseases involved in clinical data analysis or one of the most important part for forecasting. Early detection of cardiovascular diseases can help to reduce high-risk condition for heart patients to make individual decisions for their lifestyle adjustments, mitigating the challenges. Early detection of heart disease has been explored in this study using a machine-learning approach. Additionally, we used sampling strategies to deal with disparate datasets. The overall risk is estimated using a variety of machine-learning techniques. On Kaggle, the Heart Disease dataset is accessible and open for all. In present study testing set used this dataset. The ultimate objective is to determine whether the patient has a “10-year risk of developing coronary heart disease” (CHD). The dataset contained thirteen features that provided patient data, and the authors used machine learning algorithms to diagnose cardiac problems with 98.8% accuracy.

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Heart Disease Prediction Using Machine Learning Technique

Cited by 3 publications

References 8 publications

Cardiac Disease Prediction using Supervised Machine Learning Techniques.

Cardiac Disease Prediction using Supervised Machine Learning Techniques.

Application of Machine Learning Algorithm: Forecasting Heart disease

Early Detection of Heart Disease Using Machine Learning Approach

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