Abstract: Knowledgeable data is the fundamental step for discovering different types of patterns from large database. The pattern to be discovered from vast amount of data employs classification technique. Classification (i.e., classifier) builds a model with the relationship between the attribute set, class set and input data. However, most of the classification techniques do not fit with a good starting point on classifying multiple data sources class patterns. Even if it works on multiple data sources class patterns, it produces both the best and worst cases of result set. On occurrence of worst case result, patterns are not nested properly resulting in the tradeoff while fetching high class accuracy result. These drawbacks in the current work are overcome in our research work by working with sample of large quantities of information about patients and their medical conditions. In this research, an efficient framework for accurate disease diagnosis, Sequential Class Covering Rule based Homogeneous Data Classifier (SCCR-HDC) is proposed. Initially, SCCR-HDC framework uses the classifier tree to analyze medical information about patients from different dimensional level. For analyzing this classifier tree, a modern boosting based machine learning concept is introduced. The analyzed results of the tree are used for rule formation in the second step for efficient diagnosis of the disease patterns. The rule formed is applied on the training and test sample homogenous data to easily diagnosis the disease class accuracy. A sequential class covering rule is formed to extract the best result patterns in sequential manner from the current set of training data instances. Similarly, to diagnosis the normal, abnormal, critical disease patterns from the test samples, a searching process called, first order rule based general to precise searching process is performed in SCCR-HDC framework. Experiment is conducted on the factors such as class accuracy rate on disease diagnosis, classifier tree based time rate on predicting disease pattern and precision rate on categorizing disease patterns.