ENDORSEMENT OF SMALL PATIENTS POPULATION STUDY THROUGH DATA MINING CLASSIFICATION: SIGNIFICANCE TO MANIFEST DRUG INTERACTION STUDY OF CARDIOVASCULAR DOSAGE FORMULATION
Keywords:
Seven Classifiers, computatIonal statistical analysis, Physio-chemical patients data, Data mining processAbstract
Objective: A simple, sensitive, precise computational classifiers justifies the positive indication of drug interaction through statistical validation and confirms for further root level investigation.
Methods: The blood pressure (BP) & Lipid profile valued data sheet was prepared from 100 patients those were chronically treating with cardiovascular formulation consisting Atorvastatin 10mg + Olmesartan 20mg. The data sheet contains 100 patients with 10 variables and final decision attributes of working & non-working. Then, with the operation of seven different related classifier the details of % of accuracy by class, correct & incorrect classified instance and stratified cross- validation were estimated. Those statistical results of classifiers were compared, correlate and interpreted to bring a fixed conclusion based on it.
Results: The % of accuracy for all classifiers results commonly 95.9596 %, 93.9394 % and 96.9697 % and inter-depending class attributes denoting by a = NW & b =W Matrix values are 84│11, 84│9, 87│9 respectively. Thus, the accuracy is excellent covering within the limits of (±15%) as a correct classified instant.
Conclusion: Statistical computation on less populated patients through classifiers, evidentially confirms the drug-interaction profile of collected data through data mining process. So that, it can proceeds further upto root level through instrumental bioanalysis.
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