Quadratic discriminant analysis by projection

Wu, Ruiyang; Hao, Ning

doi:10.1016/j.jmva.2022.104987

Cited by 10 publications

(1 citation statement)

References 14 publications

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“…In its development, several optimization methods have been developed to speed up training process, one of which is the Adam optimization algorithm [35]. Quadratic Discriminant Analysis (QDA) is a discriminant analysis method which aims to find quadratic hypersurfaces to separate classes between data [36]. QDA can handle heteroscedasticity data, namely data that can have heterogeneous variance values.…”

Section: 𝑃(𝑥mentioning

confidence: 99%

Analysis of Data and Feature Processing on Stroke Prediction using Wide Range Machine Learning Model

Wisesty,

Wirayuda,

Sthevanie

et al. 2024

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Stroke is a disease which cause the death of brain cells, so that the part of the body controlled by the brain loses its function. If not treated immediately, this disease can cause long-term disability, brain damage, and death. In this research, stroke prediction was carried out on the Stroke dataset acquired from the Kaggle dataset using various machine learning models. Then, data sampling techniques are used to handle data imbalance problems in the stroke dataset, which include Random Undersampling, Random Oversampling, and SMOTE techniques. Pearson Correlation and Principal Component Analysis are also used for dimensional reduction and analyzing the important features that are most influential in predicting stroke. Pearson Correlation produces five attributes that have the highest Pearson coefficient, namely age, hypertension, heart disease, blood sugar level, and marital status. Experimental results have demonstrated that the utilization of RUS, ROS, and SMOTE sampling techniques can significantly boost the F1-Score testing by an impressive 43.44%, 34.44%, and 35.55% respectively, as compared to experiments conducted without implementing any data sampling techniques. The highest F1-Score testing was achieved using the Support Vector Machine and Gaussian Naïve Bayes models, namely 0.83.

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Section: 𝑃(𝑥mentioning

confidence: 99%