Evidence Based health care system using Big Data for disease diagnosis

Pasupathi, Chitra; Kalavakonda, Vijaya

doi:10.1109/aeeicb.2016.7538393

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…Modern technology allows us to collect the information found on these platforms. This valuable information can be then used to create smart applications such as self-diagnosis systems, medicine recommender systems, e-Doctor websites, cloud-assisted drug recommendation services, and many others [3,4].…”

Section: Introductionmentioning

confidence: 99%

Web application for self-diagnosis and drug recommendation based on user symptoms

2019

J. adv. tec. eng. res.

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Throughout the evolution of the Internet and social networks, forums and online platforms have a vital role in sharing information, along with the creation and engagement of virtual communities. Such websites represent great resources, and they are the irst step in the adoption of e-health services. When the persons are ill, many of them use search engines for self-diagnosis and gather possible treatment ideas before asking for a doctor's opinion. This takes a lot of time because the information is scattered across various forums and websites. In this paper is presented an application that aims to provide an online self-diagnosis and drug recommendation tool based on natural language processing of the symptoms described by the user. Over 2,200 medicines are stored in the database, each having a set of keywords according to their usage. Thus, the platform automates the search process, and provides the user with the most relevant information, eliminating the need for manual data interpretation. The results are ranked according to the con idence score obtained after the execution of the fuzzy search algorithm. The platform does not provide medical advice. Thus it is intended for informational purposes only. The developed platform is not a substitute for professional medical advice, diagnosis or treatment. Another feature of the platform is that it enables users to ind hospitals and clinics around them, so that they can receive professional healthcare service. The ield of medicine is a sensitive topic due to the fact that one mistake could lead to the loss of countless human lives. It was a long and dif icult journey to reach the current state of medicine, but technology was and will remain essential to the evolution of the healthcare system. The proposed solution of this paper is customized for Romania but it can be adapted for other countries by replacing the drug database.

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Section: Introductionmentioning

confidence: 99%

Web application for self-diagnosis and drug recommendation based on user symptoms

2019

J. adv. tec. eng. res.

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“…This amalgam model combines k-means with k-nearest neighbor (KNN) with multi-sep preprocessing. Nirmala Devi et al[11] established a customized logistic regression model for diabetes m Mellitus as well as Identifying the vital variables of type II DM.Pasupathi and Kalavakonda[12] proposed the evidence Based health care system using Big Data for disease diagnosis. Patil et al…”

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confidence: 99%

Statistical and Machine Learning Approaches for Clinical Decision on Drug Usage in Diabetes with Reference to Competence and Safeness

Balamurugan¹,

Saranya²,

Sasikala³

et al. 2021

IJCIS

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Diabetes is a chronic disease that requires patient-centered treatment. The physician strategy for treatment of diabetes varies from one patient to another. Using the clinical parameters and the evidence of diabetes at various group of people are to be treated with the drugs that provide significant changes over period of time. In this work, safety and efficiency of drug that is used for diabetes and to provide justification using statistical approach is proposed. The benefits and harm of various drugs are represented as null hypothesis and alternate hypothesis using two-tailed t test (unpaired hypothesis testing). The drugs specified are given periodically at various weeks so that the effect of each drug is identified with clinical parameters and it is summarized. The various medications that are to be imposed on various groups of people and respected hypothesis values are calculated. The post hoc power, evaluation of p value that specify the significant change in the clinical parameters are observed. With the help of this p value and the hypothesis testing, it recommends the correct specification of drugs. The drug combination such as sulfonyl urea (glibenclamide 5 mg), sulfonyl urea + sitagliptin, sulfonyl urea + vildagliptin, metformin, metformin + sitagliptin, metformin + vildagliptin were used in this study. The above drugs are given to various groups to find out the effectiveness of drug usage in diabetes. The idea is implemented with both manual and automated approach of handling patient report and to find their significant approach and thereby to provide conclusion of the drug usage for diabetes.

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