Review on Heart Disease Classification

Dube, Harshali; Madge, Shweta; Jagtap, Prajakta; Potdar, Pooja; Bhandare, Nilesh

doi:10.1109/icces48766.2020.9137880

Cited by 4 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The precision values of Support Vector Machine algorithm and Naive Bayes also are 90.85% and 77.45% respectively, followed by recall and F1 values are also ruled out by Support vector Machine classifiers with higher values than the Naive Bayes. Research work proposed a machine learning algorithm comparison of various classifiers to predict and reduce deaths due to cardiovascular diseases (Dube et al 2020). The hyperplanes are selected by the Support Vector Machine algorithm (Jayadeva, Khemchandani, and Chandra 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis And Comparison Of Prediction Of Heart Disease Using Novel Random Forest And Naive Bayes Algorithm

Pavithraa¹,

Sivaprasad²

2023

View full text Add to dashboard Cite

Aim : Prediction of heart disease using Novel Random Forest and comparing its accuracy with Naive Bayes algorithm. Materials and methods: Two groups are proposed for predicting the accuracy (%) of heart disease. Namely, the Novel Random Forest and Naive Bayes algorithm. Here we take 20 samples each for evaluation and compared. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result : The Novel Random Forest gives better accuracy (86.40%) compared to the Naive Bayes accuracy (80.08%). Therefore the statistical significance of Novel Random Forest is better than Naive Bayes algorithm. Conclusion: From the result, it can be concluded that Novel Random Forest helps in predicting heart disease with more accuracy compared to Naive Bayes algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%