Improving the Classification of chronic diseases using The Naive Bayes algorithm
Improving the Classification of chronic diseases using The Naive Bayes algorithm
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Date
2022-02
Authors
Awad hassanMohamed
Hoyam Omer Ali Abdallah
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Abstract
Manual classification of disease into different classes based on
residential patient areas is a tedious task and may vary depending
on the scenario studied. Therefore, the standard Naïve Bayesian
classification algorithm was used to classify the disease based on
several characteristics that represent their residential areas and
their numbers, and for quick and easy prediction also in the process
of classifying the areas most susceptible to chronic disease, and
the importance of the study lies in reducing the normal manual work. A methodology focused on the problem of extending the
traditional naive Bayesian model was used to classify uncertain
data. The main problem of the naive Bayes model is estimating
the conditional probability of the class, and estimating the kernel
density is a common method so the kernel density estimation
method has been extended to deal with uncertain data.This
reduces the problem to considering double integrals. For finite
kernel functions and probability distributions, the double integral
can be evaluated analytically to give a closed formula, allowing
an efficient formula-dependent algorithm in general, however,
double integral cannot be simplified in closed forms. In this case, a
sample-based approach is proposed.The remarkable experimental
results also indicate that the proposed classification method can be
promising and can be applied elsewhere and help in the diagnosis
process by patient area. The Naïve base algorithm was used to
validate the proposed method experimentally to be 90% accurate,
which proves its efficiency.