A Web-Based Disease Prediction System Using Machine Learning Algorithms and PCA

被引：0

作者：

Khan, Anushey ^{[1
]}

Huseyinov, Ilham ^{[2
]}

机构：

[1] Istanbul Aydin Univ, Grad Inst Sci, Artificial Intelligence & Data Sci, TR-34295 Istanbul, Turkiye

[2] Istanbul Aydin Univ, Software Engn Dept, TR-34295 Istanbul, Turkiye

来源：

FORTHCOMING NETWORKS AND SUSTAINABILITY IN THE AIOT ERA, VOL 1, FONES-AIOT 2024 | 2024年 / 1035卷

关键词：

Web Technology; Gaussian Naive Bayes; Support Vector Machines; Random Forest; PCA; Mode Prediction;

D O I：

10.1007/978-3-031-62871-9_9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work presents a web-based disease prediction system to diagnose diseases using machine learning algorithms and principal component analysis (PCA). Using web technology made the system accessible from any place, at any time. An ensemble of classifiers such as Gaussian Naive Bayes, Support Vector Machines, and Random Forest, have been applied to predict the diseases. Also, PCA is utilized to find influential symptoms for a certain type of disease. The performance of the system is evaluated before and after applying PCA to the dataset in terms of accuracy, precision, recall, and F1-score. As a result, the impact of PCA on the system performance is illustrated.

引用

页码：104 / 112

页数：9

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