| Optimized Support Vector Machine for Early and Accurate Heart Disease Detection | |
|---|---|
| 學年 | 112 |
| 學期 | 2 |
| 出版（發表）日期 | 2024-06-13 |
| 作品名稱 | Optimized Support Vector Machine for Early and Accurate Heart Disease Detection |
| 作品名稱（其他語言） | |
| 著者 | Chen, Tzu-chia |
| 單位 | |
| 出版者 | CRC Press |
| 著錄名稱、卷期、頁數 | Advancements in Science and Technology for Healthcare, Agriculture, and Environmental Sustainability |
| 摘要 | Many academics use data mining to predict diseases. Some approaches can predict one sickness, while others can predict several. Sickness prediction may be improved. This article provides an overview of the numerous data categorization methods available today. Algorithms represent most commonly. Classifying data involves a lot of computation. To create a disease-fighting plan that works, enormous amounts of data must be analysed. Early diagnosis, severity assessment, and prognosis are frequent. Doing so may postpone disease development, improve quality of life, and lower medical costs. This approach uses machine learning. This article classifies and predicts cardiovascular disease data using machine learning. SVM, ANN, and RF classify heart disease data. Accuracy-wise, SVM is better for heart disease classification and detection. |
| 關鍵字 | |
| 語言 | en_US |
| ISBN | 9781032708348 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125818 ) |
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