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Machine learning in medicine using JavaScript: building web apps using TensorFlow.js for interpreting biomedical datasets

Jorge Guerra Pires

Abstract

Contributions to medicine may come from different areas, and most of these areas are filled with researchers eager to contribute. In this paper, we aim to contribute through the intersection of machine learning and web development. We employed TensorFlow.js, a JavaScript-based library, to model biomedical datasets using neural networks obtained from Kaggle. The principal aim of this study is to present the capabilities of TensorFlow.js and promote its utility in the development of sophisticated machine learning models customized for web-based applications. We modeled three datasets: diabetes detection, surgery complications, and heart failure. While Python and R currently dominate, JavaScript and its derivatives are rapidly gaining ground, offering comparable performance and additional features associated with JavaScript. Kaggle, the public platform from which we downloaded our datasets, provides an extensive collection of biomedical datasets. Therefore, readers can easily test our discussed methods by using the provided codes with minor adjustments on any case of their interest. The results demonstrate an accuracy of 92% for diabetes detection, almost 100% for surgery complications, and 80% for heart failure. The possibilities are vast, and we believe that this is an excellent option for researchers focusing on web applications, particularly in the field of medicine.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding. It was produced independent by Jorge Guerra Pires at the project IdeaCoding Lab

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study used (or will use) ONLY openly available human data that were originally located at: Kaggle

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Footnotes

  • This is the 2rd round of the review process. Several changes were made upon suggestion from the reviewers, new simulations, I have written the text, and add new resources.

Data Availability

All data produced are available online at Kaggle (Machine Learning and Data Science Community)

https://www.kaggle.com/datasets/jorgeguerrapires/predicting-diabetes-with-6-features/data https://www.kaggle.com/datasets/jorgeguerrapires/1-feature-model-for-diabetes-detection https://www.kaggle.com/datasets/jorgeguerrapires/dataset-surgical-binary-classification-reduced http://robodoc.ideacodinglab.com/#/tools/diabetes

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Machine learning in medicine using JavaScript: building web apps using TensorFlow.js for interpreting biomedical datasets
Jorge Guerra Pires
Machine learning in medicine using JavaScript: building web apps using TensorFlow.js for interpreting biomedical datasets
Jorge Guerra Pires
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