Classification of medicines using naive bayes classifier

Because this method depends on chemical properties of available training data (which are limited) The first step to classify unknown drugs is to create a new database with more number of drug datasets with chemical properties such as relative molecular mass, polar surface area, number of hydrogen bo...

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Bibliographic Details
Published in:Research journal of pharmacy and technology 2018-05, Vol.11 (5), p.1940-1944
Main Authors: Indraja, Baisani, Annapurani, K
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Data mining
Datasets
Experiments
FDA approval
Generic drugs
Hydrogen bonds
International conferences
Medicine
National libraries
Pharmaceutical industry
Signal processing
Text editing
Typhoid
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Summary:Because this method depends on chemical properties of available training data (which are limited) The first step to classify unknown drugs is to create a new database with more number of drug datasets with chemical properties such as relative molecular mass, polar surface area, number of hydrogen bond donors and acceptor, hydrophobic constant and rotatable bond count are collected to avoid the phenomenon of over fitting. Based on different features, different results are obtained while predicting the drug classes. [...]feature selection is an important task while performing classification. [...]retrieve all the attribute values of fever drugs from database. 2.Calculate the sum of relative molecular mass attribute values for all the fever drugs. .. Computation of Naive Bayes algorithm for the binary classification is explained in a detailed manner. [...]future work of this paper can deal with more number of diseases instead of binary classification and other algorithm such as SVM algorithm can be used for classifying medicines.
ISSN:0974-3618
0974-360X
0974-306X
DOI:10.5958/0974-360X.2018.00360.8