http://localhost:8080/xmlui/handle/123456789/3426 2026-06-23T06:31:42Z http://localhost:8080/xmlui/handle/123456789/3430 Title: Clinical Decision Support System for Patient Centric System in Privacy Preserving Way Using Nave Bayesian Classification Authors: Shitole, Manodnya A; Wakchaure, Manoj A Abstract: —A clinical decision support system forms critical capability to link health observations with health knowledge to influence choices by clinicians for improved healthcare. Clinical decision support system, having data mining technique which helps us for extracting data which we want. The clinical decision support system gives us the advantage which of provides the better diagnosis accuracy and also minimize the diagnosis time .The large amounts of clinical data generated every day by many of healthcare system so the naive Bayesian classification can be utilized to formed valuable information to improve clinical decision support system. But the clinical decision support system is quite promising but it also having many challenges about security of patient data. So for that I propose a new privacy-preserving patient centric clinical decision support system, which helps clinician complementary to diagnose the risk of patients disease in a privacy-preserving way. As the past patients historical data are stored in cloud so patient can use it anywhere anytime and used the train naive Bayesian classifier for finding the top-k diseases without leaking any individual patient medical data. Specifically, to protect the privacy of past patients historical data, a new cryptographic tool called additive homomorphism proxy aggregation scheme is designed. Moreover, to leverage the leakage of nave Bayesian classifier, we introduce a privacy-preserving top-k disease names retrieval protocol in our system with document uploading facility. Detailed privacy analysis ensures that patients information is private and will not be leaked out during the disease diagnosis phase. In addition, performance evaluation via extensive simulations also demonstrates that our system can efficiently calculate patients disease risk with high accuracy in a privacy-preserving way. 2022-06-15T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3429 Title: Patient-Centric and Privacy Preserving Clinical Decision Support System Using Naive Bayesian Classification Authors: Shitole, Manodnya A. Abstract: In the advanced age in which the healthcare area is exploring widely in that Clinical decision support system, which uses advanced data mining techniques to help clinician make proper decisions, has received considerable attention recently. The advantages of clinical decision support system include not only improving diagnosis accuracy but also reducing diagnosis time. The large data id generated in the healthcare system time by time so every patient is provided their personal information to the doctor for making the decision but because the privacy is the major issue for healthcare system of patient. The requirement is to provide the security to the patient data from unauthorized use the privacy preserving clinical decision support system is given in the system. So in the proposed system the patient security is the main part and in that provided the security to the patient by giving the restriction to the doctor accession. In that we check the authorization of the doctor with the OTP generation because of that the data is preserved. And also the effective Naive Bayesian classification use for the patient easiness for getting the results from the doctor about the disease diagnosis also one prominent part provided in this that patient can upload the document of their so doctor will get help to diagnosis the patient. 2016-09-05T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3428 Title: Clinical Decision Support System for the Patients Efficientness in privacy Preserving Way with Naïve Bayesian Classification Authors: Shitole, Manodnya; Wakchaure, Manoj A; Boobalan, S. Abstract: — Statistics from security firms, research institutions and government organizations show that the number of data leak instances have grown rapidly in recent years. Among various data-leak cases, human mistakes are one of the main causes of data loss. There exist solutions detecting inadvertent sensitive data leaks caused by human mistakes and to provide alerts for organizations. A common approach is to screen content in storage and transmission for exposed sensitive information. Such an approach usually requires the detection operation to be conducted in secrecy. However, this secrecy requirement is challenging to satisfy in practice, as detection servers may be compromised or outsourced. In this paper, we present a privacy- preserving data-leak detection (DLD) solution to solve the issue where a special set of sensitive data digests is used in detection. The advantage of our method is that it enables the data owner to safely delegate the detection operation to a semi honest provider without revealing the sensitive data to the provider. So in the proposed system the patient security is the main part and in that provided the security to the patient by giving the restriction to the doctor accession. In that we check the authorization of the doctor with the OTP generation because of that the data is preserved. And also the effective Naive Bayesian classification use for the patient easiness for getting the results from the doctor about the disease diagnosis also one prominent part provided in this that patient can upload the document of their so doctor will get help to diagnosis the patient. 2016-08-15T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3427 Title: Survey: Techniques Of Data Mining For Clinical Decision Support System Authors: Shitole, Manodnya; Wakchaure, M.A. Abstract: Clinical decision support system, which uses advanced data mining techniques access as well store data on server. The advantages of clinical decision support system include not only providing diagno sis accuracy but also minimize diagnosis time .Typically, with large amounts of clinical data generated every day, naive Bayesian classification can be utilized to formed valuable information to improve clinical decision support system. The Clinical Decisi on support system is very flourishing but it also having some critical problems. I propose a new privacy -preserving patient-centric clinical decision support system, which helps clinician complementary to diagnose the risk of patients’ disease in a privacy-preserving way. Also , the past patients’ historical data are stored in cloud and can be used to train the naive Bayesian classifier without leaking any individual patient medical data, and then the trained classifier can be applied to determine the disease risk for new coming patients and top-k disease names are also extracted from their according to the own preferences, which is provided for protecting the privacy of past patients’ historical data, a new cryptographic tool called additive homomorphism proxy aggregation scheme is designed. Moreover, to leverage the leakage of na¨ıve Bayesian classifier, we introduce a privacy -preserving top-k disease names retrieval protocol in our system. The privacy analysis gives security t the patient information and will not be leaked out at the time of disease diagnosis phase. This can be concluding that our system can efficiently calculate patient’s disease risk with high accuracy in a privacy-preserving way. 2016-06-05T00:00:00Z