DOCAID: PREDICTIVE HEALTHCARE ANALYTICS USING NAÏVE BAYES CLASSIFICATION


Divide and Conquer — Julius Caesar


With the advancement in the field of medical research, there has been a copious increase in the data that is stored in the various public and private hospitals, clinics and other places of medical practice. This large store of data needs to be administered in a proper manner so that we can derive useful insights and conclusions using a proper analysis system. Such large amount of data is aptly handled through analyzing the unstructured or structured data by utilizing machine learning algorithms. Predictive analytics, a key component of machine learning algorithms, helps users make enhanced and supervised decisions. Visual Analytics is a tool to cost-effectively sort the exuberant and rapidly incrementing data in the field of medical research. It helps us cope with the assorted data in an organized manner, which the human brain would be able to visualize easily. This would in turn provide new innovative and potential results. This analytics not only provides structured data but also initiates structured thoughts in the mind of humans. As practitioners analyze certain anomalous situations, the process of visual analytics process would provide sorted relevant data related to it. This would in turn decrease the cost of maintaining huge amount of data.

In this paper, we employ the machine learning techniques to predict diseases for a patient using the symptoms described by them. We utilize the Naïve Bayes Classification algorithm to develop the predictive analytics system and predict aptly the diseases for the patients. In present system aids doctors with five specific diseases with the elaborated feature based classification. In addition, we used visual analytics through the help of Cytoscape Web to help interpret and visualize the final data predicted in a better manner.