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Submitted
2 May 2024
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9 May 2024
Published
14 June 2024
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Abstract

Stroke is a significant global health concern, requiring an in-depth understanding of the complex factors contributing to its occurrence. Age, body mass index (BMI), and average glucose levels are critical factors in stroke etiology. This study employed exploratory data analysis techniques to investigate the relationships between variables in a stroke prediction dataset. The research methodology included (1) dataset description, (2) data preprocessing, (3) exploratory data analysis, and (4) interpretation. Descriptive statistical analysis provided insights into the dataset's composition and variability, while data preprocessing techniques handled missing values and facilitated feature extraction. Based on exploratory data analysis, significant relationships were found between age, hypertension, heart disease, average glucose levels, and stroke. However, BMI showed a less significant role in stroke. These findings contribute to a better understanding of the factors contributing to stroke risk and may aid in developing more effective prevention strategies.

Keywords

Exploratory Data Analysis Stroke Statistical Descriptive Analysis Risk Factor

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Copyright (c) 2024 Jurnal Komputer Terapan

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Author Biographies

Muhammad Ariful Furqon, Universitas Jember

Informatika Universitas Jember

Nina Fadilah Najwa, Politeknik Caltex Riau

Sistem Informasi Politeknik Caltex Riau

Mohammad Zarkasi, Universitas Jember

Teknologi Informasi Universitas Jember

Priza Pandunata, Universitas Jember

Teknologi Informasi Universitas Jember

Gama Wisnu Fajariyanto, Universitas Jember

Informatika Universitas Jember
How to Cite
Ariful Furqon, M. A., Najwa, N. F., Zarkasi, M., Pandunata, P., & Fajariyanto, G. W. (2024). Critical Exploratory Data Analysis on Stroke Prediction Dataset. Jurnal Komputer Terapan, 10(1), 67–77. https://doi.org/10.35143/jkt.v10i1.6307
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