Comparative Evaluation of Predictive Models on Kidney, Lung Cancer and Heart Disease
Abstract
This study supports advances in machine learning to improve early detection and treatment planning for lung cancer, cardiovascular disease, and kidney disease. We compare traditional models such as decision trees and logistic regression with complex techniques such as support vector machines, random forests, and KNN and evaluate them on publicly available data. This hybrid approach uses random forest and decision tree classifiers, leveraging adaptive learning to improve model accuracy. Results showed high prediction accuracy for kidney disease and lung cancer , while prediction accuracy for heart disease was average . This difference indicates the need for better work and more information. Future studies will focus on improving cardiovascular models, addressing data uncertainty, and integrating predictive models into clinical practice to support early diagnosis and personalized treatment to improve patient outcomes. This study demonstrates the potential for machine learning to have a major impact on diagnosis and patient management.
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[23] Lung Cancer Dataset
Link:https://www.kaggle.com/code/sandragracenelson/lung-cancer-prediction/input?select=survey+lung+cancer.csv
[24] Chronic Kidney Disease Dataset
Link:https://www.kaggle.com/code/mahmoudlimam/chronic-kidney-disease-clustering-and-prediction/input
[25] Cardiovascular Disease Dataset
Link:https://www.kaggle.com/datasets/sulianova/cardiovascular-diseasedataset
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