Authors :
Yuvraj Singh; Parth Singh; Dhirender Pratap Singh; Yash Pratap Singh; Er. Natasha Sharma; Tanuj
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/msyasyz2
Scribd :
https://tinyurl.com/bdzak7rw
DOI :
https://doi.org/10.5281/zenodo.10167542
Abstract :
This research paper explores the utilization of
Machine Learning (ML) techniques in disease prediction,
specifically targeting diabetes, heart disease and lung
cancer. As healthcare increasingly adopts data-driven
decision-making through advanced data analysis and
predictive modeling, our study employs established ML
algorithms - Logistic Regression, Naive Bayes, K-Nearest
Neighbors (KNN), and Support Vector Machines (SVM) -
to accurately predict these diseases. Our primary aim is
to showcase the efficacy of these algorithms, facilitating
timely intervention and improved patient care by
healthcare professionals. We discuss the methodology,
data preprocessing, feature selection, and model
evaluation for each disease prediction task, emphasizing
data quality and ethical concerns. Through
comprehensive experimentation, we offer insights into
algorithm strengths and weaknesses, highlighting their
relevance in disease prediction. This research contributes
to medical informatics, highlighting ML's potential to
enhance disease diagnosis and prognosis, making it a
valuable resource for researchers, practitioners, and
policymakers embracing ML for healthcare
advancement.
Keywords :
Machine Learning, Disease Prediction, Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machines.
This research paper explores the utilization of
Machine Learning (ML) techniques in disease prediction,
specifically targeting diabetes, heart disease and lung
cancer. As healthcare increasingly adopts data-driven
decision-making through advanced data analysis and
predictive modeling, our study employs established ML
algorithms - Logistic Regression, Naive Bayes, K-Nearest
Neighbors (KNN), and Support Vector Machines (SVM) -
to accurately predict these diseases. Our primary aim is
to showcase the efficacy of these algorithms, facilitating
timely intervention and improved patient care by
healthcare professionals. We discuss the methodology,
data preprocessing, feature selection, and model
evaluation for each disease prediction task, emphasizing
data quality and ethical concerns. Through
comprehensive experimentation, we offer insights into
algorithm strengths and weaknesses, highlighting their
relevance in disease prediction. This research contributes
to medical informatics, highlighting ML's potential to
enhance disease diagnosis and prognosis, making it a
valuable resource for researchers, practitioners, and
policymakers embracing ML for healthcare
advancement.
Keywords :
Machine Learning, Disease Prediction, Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machines.
