Critical Exploratory Data Analysis on Stroke Prediction Dataset

Muhammad Arif Ariful Furqon; Nina Fadilah Najwa; Mohamad Zarkasi; Priza Pandunata; Gama Wisnu Fajariyanto

doi:10.35143/jkt.v10i1.6307

Authors

Muhammad Ariful Furqon Universitas Jember
Nina Fadilah Najwa Caltex Riau Polytechnic
Mohammad Zarkasi Universitas Jember
Priza Pandunata Universitas Jember
Gama Wisnu Fajariyanto Universitas Jember

DOI:

https://doi.org/10.35143/jkt.v10i1.6307

Keywords:

Exploratory Data Analysis, Stroke, Statistical Descriptive Analysis, Risk Factor

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.

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Author Biographies

Muhammad Ariful Furqon, Universitas Jember

Informatika Universitas Jember
Nina Fadilah Najwa, Caltex Riau Polytechnic

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

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