Liver transplantation data with 256 attributes were collected from 389 attributes of the United Nations Organ Sharing registry for the survival prediction. Appropriate selection of attributes and methods are necessary for the survival prediction. As Liver transplantation is the curative surgical procedure for the patients suffering from end stage liver disease, predicting the survival rate after Liver transplantation has a big impact. For extracting the features and their relationships from a huge database, various data mining techniques need to be employed. Medical databases contain large volume of data about patients and their clinical information. Based on the obtained results "OptiFog" assures best possible improvement in the performance of the Distributed Fog Computing environment. Based on the results obtained, a novel optimization approach "OptiFog" is proposed to achieve faster computation in worst-case scenarios by varying and assigning jobs to the nodes to measure performance parameters in Distributed Fog Computing. Other optimization parameters like CPU usage, number of cores, response time and available memory space, these parameters are considered and varied, to assess the performance of Heterogeneous Raspberry Pi cluster. The first parameter is data transmission time which is improvised by minimizing network overheads. Further, the performance of the Raspberry Pi cluster-systems using dispy are analyzed and optimized step by step based on different parameters. Dispy is used to facilitate the scalability and parallel data processing on a Cluster of Raspberry Pi used for Fog Computing, to enable faster decision making. In this paper, ECG signal analysis is taken as a processing job in Fog Computing. In Health Care applications the Fog Computing performance can be assessed by measuring the time elapsed between the generation of the health care data and decision-making. To enhance the computing power of the Fog node, a Cluster of Raspberry Pi having heterogeneous configurations can be used. Fog Computing saves energy, bandwidth and prevents transmission latencies but, lacks in computing power as compared to Cloud Computing. When cloud computing is used, data transmission deferrals may cause delays in the decision-making process. To achieve faster decision making, contemporary health care applications use cloud computing for such data.
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