Heart Disease Prediction Model using various Supervised Learning Algorithm

Upadhyay, Sachin; Dwivedi, Anjali; Verma, Ashutosh Kumar; Tiwari, Virendra Kumar

doi:10.1109/csnt57126.2023.10134595

Cited by 5 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To train and evaluate these models, researchers often turn to popular datasets like the Breast Cancer Dataset and the Wisconsin Breast Cancer Dataset, which are commonly utilized in the field of breast cancer diagnosis [18]. In [1], A technique has been presented to boost the performance of two distinct algorithms: Decision Tree (J48) and Naive Bayes (NB).These classifiers are employed to classify data with a 10-fold cross-validation process applied. The introduction of a resampling filter resulted in a substantial improvement in the J48 classifier's performance, attaining a precision level of 98.2%.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Accuracy of Machine Learning Classifier in the Classification of Breast Cancer Disease

Ashish Kumar, Seema Verma

2023

tjjpt

View full text Add to dashboard Cite

The Breast cancer is recognized as a highly prevalent and life-threatening condition affecting women. In various domains, ML techniques have shown to be reliable predictors. In this study, medical data from Wisconsin breast cancer dataset were used to examine and compare supervised machine learning algorithms for predicting Breast diseases. There are several classifiers with varying level of accuracy, which is the research challenge. This paper suggests a method for enhancing the efficiency and precision of four distinct classifiers: Random Forest Tree, K- nearest neighbor (KNN), Logistic Regression, and SVM. To assess the efficacy of different algorithms, the evaluation employed both the AUC score and confusion matrix. The decision to utilize an algorithm is determined by an AUC score exceeding 0.5, which serves as an assessment metric validating the algorithm's efficacy. The employment of a machine learning technique hinges on achieving this threshold AUC score. Among all the machine learning algorithms tested, the Random forest tree attained the highest AUC score in the trial, reaching 1.0.

show abstract

Section: Literature Reviewmentioning

confidence: 99%