Prediction of Heart Stroke Using Support Vector Machine Algorithm
| Autor: | Kulkarni Rakshit Raghavendra, Jhanavi Chaudhary, Kishore Bingi, Rhea Mantri, Harshita Puri |
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| Rok vydání: | 2021 |
| Předmět: |
Training set
Heart disease Computer science business.industry Quantitative Biology::Tissues and Organs information science Pattern recognition medicine.disease Support vector machine Quadratic equation Support vector machine algorithm medicine Performance prediction Smoking status Artificial intelligence business Stroke |
| Zdroj: | 2021 8th International Conference on Smart Computing and Communications (ICSCC). |
| DOI: | 10.1109/icscc51209.2021.9528241 |
| Popis: | This paper focuses on developing a prediction model to predict heart stroke using the parameters, namely, age, hypertension, previous heart disease status, average body glucose level, BMI, and smoking status. The prediction model is developed using a support vector machine (SVM) algorithm. Further, the SVM algorithm with various decision boundaries like linear, quadratic, and cubic are also produced. The performance prediction results show that the linear and quadratic SVM has performed better in predicting the heart stoke with greater accuracy values. This is true for both the male and female databases during training and testing. |
| Databáze: | OpenAIRE |
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