Detection of disease at earlier stages is the most challenging one. Datasets of different diseases are available online
with different number of features corresponding to a particular disease. Many dimensionalities reduction and feature extraction
techniques are used nowadays to reduce the number of features in dataset and finding the most appropriate ones. This paper
explores the difference in performance of different machine learning models using Principal Component Analysis dimensionality
reduction technique on the datasets of Chronic kidney disease and Cardiovascular disease. Further, the authors apply Logistic
Regression, K Nearest Neighbour, Naïve Bayes, Support Vector Machine and Random Forest Model on the datasets and compare
the performance of the model with and without PCA. A key challenge in the field of data mining and machine learning is building
accurate and computationally efficient classifiers for medical applications. With an accuracy of 100% in chronic kidney disease
and 85% for heart disease, KNN classifier and logistic regression were revealed to be the most optimal method of predictions for
kidney and heart disease respectively.
Keywords
Kidney Disease, Cardiovascular Disease, Logistic Regression, Support Vector Machine, Random Forest.
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Acknowledgements
We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
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Corresponding author
Claire Salkar
Claire Salkar
Engineering Informatica, Methodist University of Angola, Luanda, Angola.
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Cite this article
Claire Salkar, “A Detailed Analysis on Kidney and Heart Disease Prediction using Machine Learning”, Journal of Computing and Natural Science, vol.1, no.1, pp. 009-014, January 2021. doi: 10.53759/181X/JCNS202101003.