A streamlit-powered cloud platform for machine learning-driven early detection of cardiovascular diseases

Abstract

Cardiovascular diseases (CVDs) are a major contributor to global morbidity and mortality, highlighting the need for early detection and prevention. This study introduces CardioPredict AI, a cloud-based system using advanced machine learning (ML) for CVD prediction. It offers scalable, accessible, and real-time diagnosis. The system leverages a comprehensive patient dataset that integrates multiple clinical features, including age, cholesterol levels, and blood pressure. Data preprocessing involved imputation, normalization, one-hot encoding, and the selection of 12 key features. The random forest model achieved an accuracy of 90.21%, a recall of 94.75%, and an F1-score of 91.31%, meeting the medical standards for heart disease prediction (recall >90%; false negatives <20). Cross-validation yielded a recall of 0.8940 ± 0.0889. Key features include personalized recommendations, real-time risk assessment through a Streamlit application, SHapley Additive exPlanation-based interpretability, and a dashboard for patient metrics. This study highlights the potential of ML and cloud computing to reduce the burden of CVDs through early detection.

Keywords

Cardiovascular disease prediction / Random forest / Dataset merging / Machine learning / Recall optimization

Acknowledgments

We would like to thank the Department of Computer Science and Engineering at the Future Institute of Engineering and Management, Kolkata, for their valuable support throughout this project.

Funding

None.

Conflict of interest

Saurav Mallik is an Editorial Board Member of this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

Author contributions

Conceptualization: Soumita Seth, Debangshu Bhattacharjee

Data analysis: Soumita Seth, Debangshu Bhattacharjee, Anusree Dam, Provat Mondal

Data curation: Debangshu Bhattacharjee, Anusree Dam

Funding acquisition: Saurav Mallik

Methodology: Soumita Seth, Debangshu Bhattacharjee, Anusree Dam, Provat Mondal

Supervision: Soumita Seth, Saurav Mallik, Tapas Bhadra

Writing-original draft: Soumita Seth, Debangshu Bhattacharjee

Writing-review & editing: Saurav Mallik, Soumita Seth, Tapas Bhadra

Ethics approval and consent to participate

This study used publicly available, fully anonymized secondary data. According to the policy of Future Institute of Engineering and Management, Kolkata, ethics approval and informed consent were not required.

Consent for publication

This study used fully anonymized secondary data obtained from publicly available sources. As no identifiable personal information or images were used, informed consent for publication was not required under the guidelines of the Future Institute of Engineering and Management.

Availability of data

The datasets used in this study—including the Mendeley cardiovascular disease dataset, heart.csv dataset, Statlog, heart attack prediction, and heart disease dataset—are publicly available:

(i) Cardiovascular disease dataset (Mendeley): https://data.mendeley.com/datasets/dzz48mvjht/1

(ii) Heart.csv dataset: https://figshare.com/articles/dataset/heart_csv/20236848?file=36169122

(iii) Statlog Dataset: https://archive.ics.uci.edu/dataset/145/statlog+heart

(iv) Heart disease dataset (Lapp): https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset

(v) Heart attack prediction (Anand): https://www.kaggle.com/datasets/imnikhilanand/heart-attack-prediction

The code for this study is publicly available at https://github.com/CodeRishiX/Cardiovascularprediction

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