Stroke prediction based on improved machine learning algorithm

Yiheng Ren; Chenfei Wang; Haoze Wang; Yu Xia

doi:10.1117/12.2659156

23 November 2022 Stroke prediction based on improved machine learning algorithm

Yiheng Ren, Chenfei Wang, Haoze Wang, Yu Xia

Proceedings Volume 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022); 1245420 (2022) https://doi.org/10.1117/12.2659156
Event: International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 2022, Hohhot, China

Abstract

With the development of society, people's life pressure is increasing, and the number of stroke patients is also increasing year by year. At present, stroke has become the second leading cause of death in the world. Therefore, it is necessary to predict whether a person is more likely to have a stroke. At present, studies around the world have established a variety of stroke prediction models based on genetic or pathological indicators. However, the accuracy of the prediction models derived from these studies is still insufficient. This study designed a new method to predict stroke. After the datasets are collected and processed, the SMOTE method is used to balance the datasets in order to obtain better results in the future prediction. In the stage of constructing new patient features, eight complex new features with different correlations were created. After completing the construction of new features, this study uses the machine learning model algorithm built in the Sklearn function library in Python to train and predict the processed datasets. Experimental results show that the prediction accuracy of most algorithms has reached more than 95%, which has been greatly improved compared with previous studies. Finally, this study uses PyQt5 and QtDesigner to build a simple GUI, which can be easily operated by users who have the need to predict stroke.

Citation Download Citation

Yiheng Ren, Chenfei Wang, Haoze Wang, and Yu Xia "Stroke prediction based on improved machine learning algorithm", Proc. SPIE 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 1245420 (23 November 2022); https://doi.org/10.1117/12.2659156

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KEYWORDS

Machine learning

Data modeling

Human-machine interfaces

Computing systems

Data conversion

Data processing

Detection and tracking algorithms

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