AN EFFICIENT CLUSTER SYSTEM FOR BIO-INFORMATICS DATA USING AMALGAM OF CLUSTERING METHODS
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 10, Pages 1958-1971
AbstractAt this pandemic Covid-19 situation, Machine learning is inspired to develop an automated drug evaluation system for the patients suffering with routine illness conditions from the existing valid drug bank using a blend of clustering methods. To get effective win over the corona virus we have to use technological advances to prevent the pandemic disease by avoiding physical contacts as well as use Artificial Intelligence like Chest Scan image processing to predict covid-19 virus in lungs by using proper algorithm to differentiate Corona lobes with other disease lobes. In this scenario I would like to use computer science knowledge based on the physio chemical properties and enzyme inhibition properties of drug dataset provided by standard drug bank repository i.e., www.drugs.com,www.drugbank.ca  and www.malacards.org.Here I applied existing clustering techniques as a blend of k-means, k-medoids, hierarchical methods and Fuzzy k-means to determine an appropriate set of drugs from the given drugbank for different illness conditions of thyroid patients. In this research work data preparation for drug evaluation is playing a crucial role, we used Correspondence Analysis (CA) method to make our Cluster system is efficient and effective. We have shown the analysis as a blend of cluster methods successful for this cluster system using graphical presentation and derived best hybrid cluster system as final outcome.
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