Medicine Allotment for COVID-19 Patients by Statistical Data Analysis

Vardhini, P. A. H.; Prasad, S. S.; Korra, S. N.; Ieee,

Computational intelligence deals with the development and application of computational models and simulations, often coupled with high performance computing, to solve complex physical problems arising in engineering analysis and design as well as natural phenomena. COVID-19 is a coronavirus-induced infectious disease. Most people worldwide got infected with this virus and became mild to moderately ill with respiratory related problems. Most infected individuals who experienced mild to moderate illness/ disease and without hospitalization recovered. Yet older people with underlying medical conditions are more likely to experience severe diseases, such as cardiovascular disease, diabetes, chronic respiratory disease, and cancer. As specific vaccine or treatment for COVID-19 is not yet prescribed, it is a tough task to prescribe a common medicinal procedure. There are many ongoing clinical trials evaluating potential treatments. This work presents an application that allots medicines to the one who tested positive. This proceeds after checking patients medical data which include BP, diabetes, cancer, alcoholic habits etc.,. Variations in the patient data originated from various sources with several medical concerns with different specifications is useful in evaluating and allotting proper medical course for COVID- 19 patient treatment. Number of attributes are used in creating the database. Different ages are categorized and the corresponding treatment will be prescribed based on the age category and the medical history of the patient. Missing data values can affect the data sets and the performance of data mining system. This work presents clustering methods which is a method of unsupervised learning and common technique for statistical data analysis. Various clustering algorithms with test samples are carried out for medicine allotment based on age category, symptoms and medical history to evaluate the respective accuracy score.