Predictive method for the detection of hepatitis with various machine learning technique
DOI:
https://doi.org/10.61841/179w8495Abstract
History: The classification and estimation of data in medical data mining is not just a matter of
precision, but also the issue of life and death. A false decision can disastrously impact patients
and their families' lives.
The design of the classifications that work on the type of structural parameter selected is a
foundation of traditional classification problems. If a floating classificator, the rules, history,
consequence etc. act as the structural parameters, the distance metric in the classificator KNearest Neighbor (KNN); and the number of hidden layers, weights, and partialities in the
Artificial Neural Network act as the structural parameters. Tuning of these parameters is a hectic
task.
Methods: For each of our datasets we use different classification algorithms including decisiontrees,knn,svm,extra-tree,adaboost,lightgbm and measured the exactness, score and cross of each
classifier validated.
Our method is checked by original-world data sets and present our findings compared to
previous studies' latest results.
Performance: The results of our data set study showed that we performed the Decision trees, KNearest and Support Vector Machines more efficiently than the Neural Network.
Conclusions: The approach can be used for different disease forecasts and diagnoses in
healthcare as an intelligent learning device.
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References
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