High-Performance Disease Prediction and Recommendation Generation Healthcare System Using I3 Algorithm

Abstract

The increase in number of disease challenges medical practitioner in making right decisions. As most diseases have the same set of symptoms, the medical practitioner struggles to take decision on recognizing disease as well as right treatment methods. A number of approaches are available for the disease identification and providing treatment, but finding the right approach is what matters. To solve this issue, an inter-/intra-disease impact, the disease-based prediction and recommendation generation method, is presented. The method first reads the input data set and produces a series of clusters with the samples obtained. In the second level, the method estimates inter-disease impact measure and intra-disease impact measure on various disease classes for every data point of the data collection. Using these two measures, the method computes I2-Disease weight for each data point in assigning a label to the data points. For the classification, the method estimates symptomatic disease weight based on inter-/intra-symptom correlation assessment. Based on the selected disease class, a set of treatment samples is populated and ranked according to their curing rate.