Authors :
Vrutika Bagul; Vrushali Bagul; Sadichha Patil; Swati Bhoir
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/mvbzytke
Scribd :
https://tinyurl.com/35p5e5y5
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR1453
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Machine learning, which is a type of computer
technology, has changed healthcare a lot. It helps doctors
predict diseases better and faster. In healthcare, using
machine learning algorithms decision tree (DT), logistic
regression (LR), support vector machine (SVM) that can
help predict lots of different diseases at the same time.
This helps doctors find and treat illnesses early, which
makes patients better and saves money on healthcare.
This paper looks at how we can use computer programs
that learn from data to predict many diseases. It talks
about why this is good, what problems we might face, and
where we might go next with it. We give a summary of
the several machine learning models and information
sources that are often employed in illness prediction. We
also go over the significance of feature selection, model
assessment, and combining several data modalities for
improved illness prediction. We give a summary of the
several machine learning models and information sources
that are often employed in illness prediction. We also go
over the significance of feature selection, model
assessment, and combining several data modalities for
improved illness prediction. The research shows that
using machine learning algorithms to predict many
diseases at once could really help public health. Again, we
use a machine learning model to determine whether or
not an individual is impacted by a few diseases. This
training model trains itself to predict illness using sample
data.
Keywords :
Disease Prediction, Disease Data, Machine Learning, Decision Tree (DT), Logistic Regression (LR), Support Vector Machine (SVM).
Machine learning, which is a type of computer
technology, has changed healthcare a lot. It helps doctors
predict diseases better and faster. In healthcare, using
machine learning algorithms decision tree (DT), logistic
regression (LR), support vector machine (SVM) that can
help predict lots of different diseases at the same time.
This helps doctors find and treat illnesses early, which
makes patients better and saves money on healthcare.
This paper looks at how we can use computer programs
that learn from data to predict many diseases. It talks
about why this is good, what problems we might face, and
where we might go next with it. We give a summary of
the several machine learning models and information
sources that are often employed in illness prediction. We
also go over the significance of feature selection, model
assessment, and combining several data modalities for
improved illness prediction. We give a summary of the
several machine learning models and information sources
that are often employed in illness prediction. We also go
over the significance of feature selection, model
assessment, and combining several data modalities for
improved illness prediction. The research shows that
using machine learning algorithms to predict many
diseases at once could really help public health. Again, we
use a machine learning model to determine whether or
not an individual is impacted by a few diseases. This
training model trains itself to predict illness using sample
data.
Keywords :
Disease Prediction, Disease Data, Machine Learning, Decision Tree (DT), Logistic Regression (LR), Support Vector Machine (SVM).
