Abstract:
In the current era in which we are living data is very important in every field of life. Data allows
associations to quantify the adequacy of a given technique, When systems are instituted to
conquer a test, collecting data will allow you to decide how well your good answer is
performing, and your methodology should be changed or changed over the long haul. Therefore,
for a better understanding and decision, we need well-arranged data. However, unfortunately, we
do not get it initially. For this purpose, we need to visualize the data in such a way that we can
classify between different classes. Until we do not properly classify data, we will not be able to
make a good decision based on data. We have different visualization (like scatter plot, histogram,
Chernoff faces, Pixel-oriented visualization, Geometric Projection Visualization, Icon-Based
Visualization, Hierarchical Visualization, Visualizing Complex Data, and Relations, etc.) and
classification (SVM, Decision tree, random forest, Naive Bayes, etc.) approaches for numeric
data[1]. We introduce a novel approach for numeric data to increases the efficiency of the data to
do a better understanding. Our approach is a combination of the visualization technique Chernoff
faces[2] and the CNN model, which is used for the classification of images. For the validation of
our approach, we use critical medical data hepatitis C Virus (HCV). This is very important
according to the patient perspective. HCV is growing very fast worldwide. And it is the major
global cause of death. It is not very dangerous in the early stages but can be deadly later on. The
cause of HCV differs from country to country. In Pakistan, the key factor of HCV is the reuse of
glass syringes. In Europe and America, the cause of HCV is the use of unsafe drug injections.
Classification in this type of dataset is very important because it can save a life. Without using
our approach, the efficiency of the data was about 22% to 26% and after using our proposed
approaches the efficiency of data to increase up to 99%.
