Authors :
Dr. P. Bhaskar; V. S. Rithesh Kumar Burramsetty; Bhavya Pinnaka; Brahma Teja Kalapala; V. S. Sudheer Kumar Tanguturi
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://shorturl.at/aEPT4
Scribd :
https://shorturl.at/oAJP2
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR709
Abstract :
This study investigates how machine learning
(ML) techniques may be used to forecast health
indicators' accuracy, which is important for efficient
medical monitoring and diagnosis. Numerous machine
learning techniques, such as Support Vector Machines
and Random Forest, are evaluated by using a
heterogeneous dataset that includes vital signs, lab
findings, and patient information. Model performance
is optimised by careful preprocessing and feature
engineering, which includes managing missing variables
and normalisation. Model accuracy is further improved
via hyperparameter tuning strategies, which are
measured using metrics like precision and recall. The
findings show that machine learning (ML) models can
accurately predict health index accuracy, which may
help with early illness identification and individualised
treatment plans. The study highlights the potential of
machine learning in healthcare decision-making and
provides guidance for raising the standard of patient
care. Future projects could look into adding more
functionality and integrating real-time data for.
Keywords :
LSTM, Random Forest, Gradient Boosting, Decision Tree, Linear Regression.
This study investigates how machine learning
(ML) techniques may be used to forecast health
indicators' accuracy, which is important for efficient
medical monitoring and diagnosis. Numerous machine
learning techniques, such as Support Vector Machines
and Random Forest, are evaluated by using a
heterogeneous dataset that includes vital signs, lab
findings, and patient information. Model performance
is optimised by careful preprocessing and feature
engineering, which includes managing missing variables
and normalisation. Model accuracy is further improved
via hyperparameter tuning strategies, which are
measured using metrics like precision and recall. The
findings show that machine learning (ML) models can
accurately predict health index accuracy, which may
help with early illness identification and individualised
treatment plans. The study highlights the potential of
machine learning in healthcare decision-making and
provides guidance for raising the standard of patient
care. Future projects could look into adding more
functionality and integrating real-time data for.
Keywords :
LSTM, Random Forest, Gradient Boosting, Decision Tree, Linear Regression.
