Rough – Granular Computing knowledge discovery models for medical classification

Eissa, Mohammed M.; Elmogy, Mohammed; Hashem, Mohammed

doi:10.1016/j.eij.2016.01.001

Cited by 15 publications

(10 citation statements)

References 7 publications

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“…It operates in two modes. In GC, one may start with fuzzy side and move downward or start with the crisp side of the problem and move upwards [39]. Chatbot is an intellectual simulated chatting program where a machine interacts with the user.…”

Section: Eai Endorsed Transactions Onmentioning

confidence: 99%

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

Sharma

Singh

2018

ICST Transactions on Scalable Information Systems

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The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.

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Section: Eai Endorsed Transactions Onmentioning

confidence: 99%

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

Sharma

Singh

2018

ICST Transactions on Scalable Information Systems

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“…The most frequently used are rough set (e.g. Eissa et al 2016;Skowron et al 2012;Stepaniuk 2008;Pal et al 2005) and fuzzy set (e.g. Ray et al 2016;Kundu and Pal 2015;Pal et al 2012;Ganivada et al 2011) approaches.…”

Section: Granular Computingmentioning

confidence: 99%

Recent granular computing frameworks for mining relational data

Hońko

2018

Artif Intell Rev

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A lot of data currently being collected is stored in databases with a relational structure. The process of knowledge discovery from such data is a more challenging task compared with single table data. Granular computing, which has successfully been applied to mining data storable in single tables, is a promising direction for discovering knowledge from relational data. This paper summarizes some recent developments in the area of application of granular computing to mining relational data. Four granular computing frameworks for processing relational data are introduced and compared. The paper shows how each of the frameworks represents relational data, constructs information granules and build patterns based on the granules. A generic system that can employ any of the frameworks to discover knowledge from relational data is also outlined.

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“…Nowadays, Data Mining (DM) and Knowledge Discovery Process (KDP) plays a vital role due to the growing size of the data over the cloud platform specifically on medical application with respect to the symptoms of diseases and how to predict and diagnose it correctly (Wang et al 2019;Vaidya et al 2014;Eissa, Elmogy, and Hashem 2016). In clinical predictions model, KDP pursues toward gathering knowledge through finding a relationship amongst numerous data attributes (Ma et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…In clinical predictions model, KDP pursues toward gathering knowledge through finding a relationship amongst numerous data attributes (Ma et al 2019). On the other hand, the knowledge discovery process supports medical experts towards preventing false prediction results, diagnostic, identified the relationships amongst different medical indicators, detection of diseases as well as enhance the process of treatment decision making (Eissa, Elmogy, and Hashem 2016). In order to analysing these kind of data, researchers, governments and most of the organizations offered enhanced services as well as improve value to their relations with their customers, users, patients and so on (Parikh, Kakad, and Bates 2016; Moloud Abdar et al 2019; Darcy, Louie, and Roberts 2016).…”

Section: Introductionmentioning

confidence: 99%

Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

Radhika*,

Priya

2020

IJITEE

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Predictive modelling is a mathematical technique which uses Statistics for prediction, due to the rapid growth of data over the cloud system, data mining plays a significant role. Here, the term data mining is a way of extracting knowledge from huge data sources where it’s increasing the attention in the field of medical application. Specifically, to analyse and extract the knowledge from both known and unknown patterns for effective medical diagnosis, treatment, management, prognosis, monitoring and screening process. But the historical medical data might include noisy, missing, inconsistent, imbalanced and high dimensional data.. This kind of data inconvenience lead to severe bias in predictive modelling and decreased the data mining approach performances. The various pre-processing and machine learning methods and models such as Supervised Learning, Unsupervised Learning and Reinforcement Learning in recent literature has been proposed. Hence the present research focuses on review and analyses the various model, algorithm and machine learning technique for clinical predictive modelling to obtain high performance results from numerous medical data which relates to the patients of multiple diseases.

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Rough – Granular Computing knowledge discovery models for medical classification

Cited by 15 publications

References 7 publications

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

Recent granular computing frameworks for mining relational data

Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

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