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- W3201424909 abstract "Cardiovascular diseases or heart-related diseases are one of the most significant reasons for a huge number of mortalities in the world over the past few decades and proved to be the most life-threatening disease. The person gets attacked by this disease immediately that he hardly gets any chance for treatment. So, it is a challenging task for doctors to diagnose the patients timely and correctly. So, there is a requirement for an achievable, reliable and accurate method to detect such illness in real time for genuine medications. The healthcare organization gathers a huge amount of data related to heart disease, but unfortunately, they are not mined for discovering the unseen pattern and potent decision making by doctors. The paper aims at developing cost-effective treatment and facilitating a database decision support system by using various data mining techniques like the state-of-the-art approach artificial neural network (ANN), AdaBoost, decision tree, Passive Aggressive, logistic regression, voting classifier, Naïve Bayes, K-Nearest Neighbors (KNN), support vector (SVC) and random forest. Dimensionality reduction techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been discussed for minimizing the number of attributes to increase the performance of the machine learning algorithms. Artificial neural network was the best algorithm with an accuracy of 88.52% with PCA and 85.24% with LDA dimensionality reduction technique." @default.
- W3201424909 created "2021-09-27" @default.
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- W3201424909 date "2021-09-20" @default.
- W3201424909 modified "2023-10-16" @default.
- W3201424909 title "Early-Stage Coronary Ailment Prediction Using Dimensionality Reduction and Data Mining Techniques" @default.
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- W3201424909 doi "https://doi.org/10.1007/978-981-16-3346-1_58" @default.
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