Heart Disease Prediction System based on Decision Tree Classifier

Bavani, B.; Rajini, S. Nirmala Sugirtha; Josephine, M.S.; Prasannakumari, V.

doi:10.5373/jardcs/v11sp10/20192968

Cited by 8 publications

(1 citation statement)

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“…People in the field of business frequently used machine learning algorithms in financial research. The paper focuses on the accuracy of A Khemphila, V Boonjing -2010. evaluating housing prices in each of the 50 states using Classification Function Algorithms(CFA) and Long Short-Term Memory (LSTM), two different forecasting techniques developed and motivated by (Bavani et al 2019). The strategies take into account all of the K = 2m distinct model combinations in each time period t when there are m predictors available The limitation of the proposed work is due to inconsistent data and difficulty in getting the right datasets for analysis(Sivaranjani, Naresh, and Murthy 2019; Purushottam et al 2015).…”

Section: Discussionmentioning

confidence: 99%

Heart Disease Prediction Based on Age Detection using Novel Logistic Regression over Decision Tree

Karthi¹,

Kalaivani²

2023

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Aim: To improve the accuracy in Heart Disease Prediction using Novel Logistic Regression and Decision tree Materials and Methods: This study contains 2 groups i.e Novel Logistic Regression and Decision tree Each group consists of a sample size of 10 and the study parameters include alpha value 0.01, beta value 0.2, and the Gpower value 0.8. Results: The Novel Logistic Regression (91.60) achieved improved accuracy than the Decision Tree (89.42) in Heart Disease Prediction. The statistical significance difference is 0.01 (p<0.05). Conclusion: The Novel Logistic Regression model is significantly better than the Decision Tree in Heart Disease Prediction. It can be also considered as a better option for Heart Disease Prediction.

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Section: Discussionmentioning

confidence: 99%