Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis

Thakkar, Harshil; Shah, Vaishnavi; Yagnik, Hiteshri; Shah, Manan

doi:10.1016/j.ceh.2020.11.001

Cited by 62 publications

(33 citation statements)

References 50 publications

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“…The administration does not really treat electives that include heart pain, lung disorders, skin and liver disorders, nerve damage, and noise damage; quick findings are rare. Thakkar et al [ 54 ] has discussed some valued techniques to excavate in the future and for the diagnosis of diabetes.…”

Section: Applications Of Big Data and Aimentioning

confidence: 99%

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Yang

Islam

Noor

et al. 2021

Computational and Mathematical Methods in Medicine

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Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people’s lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

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Section: Applications Of Big Data and Aimentioning

confidence: 99%

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Yang

Islam

Noor

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…To achieve efficiency in diagnosis and monitoring results, more inputs can be defined. Thakkar et al [26] describe the prediction of diabetes disease with FIS so that users can easily use this application to perform the early action against diagnosis and rapid treatment. Al-Behadili et al [27] define the uses of the Fuzzy decision process Diabetes based on Fuzzy logic.…”

Section: Related Workmentioning

confidence: 99%

Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

Alam¹,

Shaukat²,

Khelifi³

et al. 2022

Computers, Materials &Amp; Continua

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Disease diagnosis is a challenging task due to a large number of associated factors. Uncertainty in the diagnosis process arises from inaccuracy in patient attributes, missing data, and limitation in the medical expert's ability to define cause and effect relationships when there are multiple interrelated variables. This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things (IoT) empowered by the fuzzy inference system (FIS) to diagnose various diseases. The Fuzzy System is one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties, and fuzzy logic is the best way to handle uncertainties. Our proposed system differentiates new cases provided symptoms of the disease. Generally, it becomes a time-sensitive task to discriminate symptomatic diseases. The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently. Different coefficients have been employed to predict and compute the identified disease's severity for each sign of disease. This study aims to differentiate and diagnose COVID-19, Typhoid, Malaria, and Pneumonia. This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms. MATLAB tool is utilised for the implementation of FIS. Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms. The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases. This study may assist doctors, patients, medical practitioners, and other healthcare professionals in early diagnosis and better treat diseases.

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“…For any component, A of universe X, enlistment work μF(A) indicates how much A is a component of set F. A degree having a value somewhere in the range of 0 and 1, which addresses the degree of enlistment, is called the participation worth of component A in set F. 27…”

Section: Fuzzy Setmentioning

confidence: 99%

An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis

Nagaraj

Deepalakshmi

2022

Int J Imaging Syst Tech

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Diabetes is one of the most common and hazardous diseases, which can affect almost every organ in the body. Diagnosis of diabetes requires determining all vital parameters related to the disease. However, the nature of the data from those parameters is very uncertain, affecting the process of disease diagnosis. This article proposes an intelligent fuzzy inference rule-based predictive diabetes diagnosis model (IFIR_PDDM), providing content recommendations to patients with diabetes. The suggested model employs an inference technique that medical specialists have validated for recommendations. IFIR_PDDM comprises three elements used to forecast the risk of diabetes disease. Initially, a fuzzy membership function utilizes medical recommendations and statistical methodologies. Medical specialists then validate the mining-based rules using a decision tree rule induction technique. The proposed model predicts the risk of diabetes disease using fuzzy inference based on Mamdani's technique. Based on this information, the recommendations for a normal life, nutrition, exercise, and medications are given to patients. We used an electronic health record (EHR) medical and clinical dataset from the PIMA Indian Diabetes dataset to develop our proposed model and assess its performance. The proposed model takes less time for diabetes diagnosis, and the expert recommendation system uses the fuzzy inference method.

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Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis

Cited by 62 publications

References 50 publications

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis

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