(5) logistic regression, and (6) Poisson regression. Conclusion: In this research, it was observed that the decision tree method outperforms the logistic regression approach in detecting lung cancer in the datasets that were considered. Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. The novel decision tree algorithm has achieved the significance value of 0.044 when compared to the logistic regression algorithm. Results: Novel decision tree has achieved the significance accuracy of 94.86 % compared to 80.11 %, by logistic regression algorithm. Group 1 represents the novel decision rate algorithm and group 2 represents the logistic regression algorithm.The G power calculation was done with 80 % of power and alpha of 0.05. Analogously, 2 coincides with the (partial) squared multiple correlation in multiple regression/correlation F-tests (cf. It is hoped that a desired sample size of at least 150 will be. Methods and Materials: A total of 304 samples are collected from three lung cancer datasets available in kaggle. Power Analysis for Logistic Regression: Examples for. We introduce and study a family of robust estimators for the functional logistic regression model whose robustness automatically adapts to the data thereby leading to estimators with high efficiency in clean data and a high degree of resistance towards atypical observations. R-bloggers - blog aggregator with statistics articles generally done with R software.Novel Decision Tree Algorithm, Logistic Regression Algorithm, Lung Cancer, Image Processing, False detection, Machine Learning AbstractĪim: The objective of this research work is to maximise the early detection rate of lung cancer using the novel decision tree algorithm in comparison with the logistic regression algorithm.
0 Comments
Leave a Reply. |