Through the advent of technological progression, different computer aided applications were introduced during last four decades to supplement the diagnosis and treatment phases of patient care. Now at different levels initiatives have been taken to encourage the medical practitioners for implementing these high-tech computer applications in their everyday clinical practices to enhance the graph of human well-being. Clinical decision support systems (CDSSs) were introduced as an ideal computer based application to influence the medical diagnosis process with its capability to store large extent of data and provide prerequisite data at the time of patient evaluation phases without wasting time. However the efficient progression of CDSS impeded by a number of obstacles which if addressed could potentially unlock the significance of these systems. This research work reveals the comprehensive detail of the different CDSSs that were proposed during the last four decades after the innovation of these computer systems and then draw attention towards the desirable features of CDSSs that were found as the research gaps during literature review. This study is conducted with an aim to provide a CDSSwhichisproficientenoughtoovercomethegrandchallengesthatwerearoseduring the effective deployment of these systems. ThisresearchworkpresentsanonlineKnowledgeBasedClinicalDecisionSupportSystem (KBCDSS) that is deployed as an effective prototype application in medical domain to significantly aid medical experts in their routine clinical practices. KBCDSS is a multiple disease diagnosis system with the proficiency to gather medical experts over a single platform through web. This system follows the pattern of Knowledge Data Discovery (KDD) process to extract the knowledge that is prerequisite in the patient evaluation stages. In order to accomplish an effective functioning of this system certain course of action is followed for data analysis on the Wisconsin breast cancer data set from the UCI Machine Learning Repository and implements that medical data set on the proposed system. The proposed KBCDSS initially pursues the pre-processing steps of KDD process to perform the knowledge acquisition task and proposed knowledge acquisition algorithm which efficiently update prerequisite medical data into data warehouse. The data warehouse server retains different medical records that are stored in relational tables. Using thetechniqueofDBMSwehaveproposedanalgorithmfortheconstructionofKnowledge Base (KB) and its representation. Toperformminingtask,wehaveproposedhybridCaseBaseReasoning(CBR)cyclewhere CBRandSupportVectorMachine(SVM)areusedasaninferencemechanismtocarryout more accurate conclusion and results. CBR is implemented as a core methodology in our model and we have proposed case retrieval algorithm for case retrieval phase of the CBR technique that are retrieving similar cases from KB. After that reinstantiation strategy is implemented in case reuse phase of the CBR technique for case adaption, that is simply copy the diagnosis of most similar case being the suggested solution of new input case. In case, alike cases are not present in KB, then we employ SVM for predicting the solution for new case. SVM is used for classification of data as well as predict the solution of new input case. After that, the concept of Group Clinical Decision Making (GCDM) is implemented in case revise phase where number of experts of same medical domain gives their opinion for the solution of new input case. For positive opinion from medical experts,newcaseisnowkeptintoKBwhichisthepartofrelationalDBforfutureguidance. The proposed KBCDSS is competent enough to provide comprehensive structural knowledge to its users within the very short span of time which is extremely supportive during the process of diagnosis and treatment of diseases. The efforts to develop this application were aimed to fulfill the research gaps and strengthen the weakness of previously existing CDSSssothatthedeploymentofthesecomputerbasedsystemsbecomegeneralandevery medical personals can also easily use these systems by their own without the supervision of computer experts during the patient-care phases.