Machine Learning-Powered Clinical Predictions: from Data to Deployment

EasyChair Preprint no. 13490

Abstract

The integration of sophisticated machine learning algorithms
into clinical applications has the potential to transform
healthcare by providing highly accurate predictive models. This
case study focuses on the design, development, and evaluation of
clinical predictive applications, with a primary emphasis on machine
learning methodologies. The article begins by elucidating
the motivation for this initiative, emphasizing the urgent need
for advanced predictive models to improve healthcare outcomes.
The architectural design and implementation of the application
are discussed, highlighting the central role of machine learning
at each stage.The study details the comprehensive integration of machine
learning algorithms, covering crucial aspects such as data preprocessing,
feature extraction, model training, validation, and
deployment. Various machine learning techniques, including classification,
regression, and clustering, are rigorously analyzed for
their effectiveness in predicting clinical outcomes, with a specific
focus on pain prediction. The study examines the performance
of different models and their respective algorithms, providing a
detailed comparison to determine the most effective approaches.
The paper
concludes with a thorough discussion of the research outcomes,
highlighting the significant advantages, potential limitations, and
future research directions in the application of machine learning
to clinical prediction.
This work underscores the transformative power of machine
learning algorithms in developing robust, scalable, and highly
accurate medical applications, demonstrating a substantial advancement
in healthcare predictive capabilities.

Keyphrases: APIs, Appointment System, Disease Information, Disease Prediction, logistic regression, machine learning, medication information, MongoDB, NodeJS, ReactJS, Symptoms Checker

@Booklet{EasyChair:13490,
  author = {Ayush Nautiyal and Sagar Negi and Harshit Bajpai and Chinmay Raj Shah},
  title = {Machine Learning-Powered Clinical Predictions: from Data to Deployment},
  howpublished = {EasyChair Preprint no. 13490},

  year = {EasyChair, 2024}}