Anemia diagnosis by fuzzy logic using LabVIEW

Shaik, Mahammad Firose; Subashini, M. Monica

doi:10.1109/i2c2.2017.8321790

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…It has already been shown the application of fuzzy logic and its effectiveness in the medical diagnosis of Ankylosing Spondylitis, anemia, dengue, thyroid, Alzheimer, Blood pressure, diabetes, and mental health, ontology-aided food and drug recommendation systems for a patient, etc. for identifying various disorders [5,[9][10][11][12][13][14][15]. Similarly, a lot of research is also going on to create an accurate, economical, and effective fuzzy logic-based expert system for diagnosing heart disease because the world health organization (WHO) reported that cardiovascular diseases (CVD) are now the main cause of death worldwide [5,[16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of heart diseases: A fuzzy-logic-based approach

Ali,

Sadi,

Goni

2024

PLoS ONE

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Cardiovascular diseases (CVD) also known as heart disease are now the leading cause of death in the world. This paper presents research for the design and creation of a fuzzy logic-based expert system for the prognosis and diagnosis of heart disease that is precise, economical, and effective. This system entails a fuzzification module, knowledge base, inference engine, and defuzzification module where seven attributes such as chest pain type, HbA1c (Haemoglobin A1c), HDL (high-density lipoprotein), LDL (low-density lipoprotein), heart rate, age, and blood pressure are considered as input to the system. With the aid of the available literature and extensive consultation with medical experts in this field, an enriched knowledge database has been created with a sufficient number of IF-THEN rules for the diagnosis of heart disease. The inference engine then activates the appropriate IF-THEN rule from the knowledge base and determines the output value using the appropriate defuzzification technique after the fuzzification module fuzzifies each input depending on the appropriate membership function. Moreover, the fusion of web-based technology makes it suitable and cost-effective for the prognosis of heart disease for a patient and then he can take his decision for addressing the problem based on the status of his heart. On the other hand, it can also assist a medical practitioner to reach a more accurate conclusion regarding the treatment of heart disease for a patient. The Mamdani inference method has been used to evaluate the results. The system is tested with the Cleveland dataset and cross-checked with the in-field dataset. Compared with the other existing expert systems, the proposed method performs 98.08% accurately and can make accurate decisions for diagnosing heart diseases.

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Section: Related Workmentioning

confidence: 99%

Diagnosis of heart diseases: A fuzzy-logic-based approach

Ali,

Sadi,

Goni

2024

PLoS ONE

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“…This process is known as Fuzzification. Also, the conversion of if-then rules (which are expandable depending on the user application) to crisp data is known to be a defuzzification process [12]. The candidacy value is formed based on these Fuzzy based rules.…”

Section: About Fuzzy and Its Rulesmentioning

confidence: 99%

Dynamic HUB Selection Process in Wireless Body Area Network (WBAN)

Shaik¹,

Subhashini²,

Amrutha³

2022

Coding Theory - Recent Advances, New Perspectives and Applications

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Wireless body area network (WBAN), a part of WSN, plays a pivotal role in the remote health monitoring system, these days. Wireless sensor nodes placed in, on, or around the human body are used to create WBAN. This WBAN is mainly used for collecting physiological and vital signals from humans in real-time using sensor nodes. It consists of different sensor nodes and hub, which collects the data from sensor nodes and send them to the gateway. High data rates at HUB cause the damage of an organ receiving high temperature in tissue by electromagnetic signals for a long period. In this chapter, by considering parameters such as the specific absorption rate, Battery Level, Priority of sensor nodes, and signal to noise interference (SINR) a HUB is selected dynamically, which shares the work of the HUB among different sensor nodes. So that workload on HUB decreases and shares its work accordingly to other sensor nodes concerning the data collected through the software LabVIEW. This chapter also illustrates the network (testbed) created using sensors for practically making the change in HUB by using the microcontroller, power, LM 35, BP sensor, Heartrate sensors arranged in a network through Arduino programming. In both these cases, the negative effect of electro-magnetic signals in WBAN, and the tissue damage in humans reduce for remote-health monitoring while increasing the network lifetime.

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“…They have use HGB, HCT, MCV, MCHC, WBC, Reticulocyte, Total Iron Binding Capacity (TIBC), Serum iron, and HSWC (hyper segmented white cells) laboratory test results as input parameters. As output, they use six anemia types which are Aplastic, Sideroblastic, Megaloblastic, Chronic, Myelophthisic, and Iron deficiency anemias [21]. Dalvi and Vernekar have made a study to determine the most suitable method to classify Red Blood Cells for anemia diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Diagnosing Hematological Disorders Using Deep Learning Method

Karagül

Yurtay

Öneç

2021

Sakarya University Journal of Computer and Information Sciences

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Deciding on the diagnosis of the disease is an important step for treating the patients. Also, the numerical value of blood tests, the personal information of patients, and most importantly, an expert opinion is necessary to diagnose a disease. With the development of technology, patient-related data are obtained both rapidly and in large sizes. Deep learning methods, which can produce meaningful results by processing the data in raw form, are beginning to give results that are close to human opinion nowadays. The present work is aimed to develop a system that will enable the diagnosis of anemia in general practice conditions due to the increasing number of patients and the intention of the hospitals, as well as the difficulties in reaching the expert medical consultant. The main contribution of this work is to make a diagnosis like a doctor with the data as the way the doctor uses it. The data set was obtained from the actual hospital environment and no intervention, such as increasing or decreasing the number of data, increasing or decreasing the number of attributes, reduction, integration, imputation, transformation, or discretization, has been made on the incoming patient data. The original hospital data are classified for the diagnosis of anemia types and the accuracy of 84,97% achieved by using a deep learning algorithm.

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Anemia diagnosis by fuzzy logic using LabVIEW

Cited by 11 publications

References 4 publications

Diagnosis of heart diseases: A fuzzy-logic-based approach

Diagnosis of heart diseases: A fuzzy-logic-based approach

Dynamic HUB Selection Process in Wireless Body Area Network (WBAN)

Diagnosing Hematological Disorders Using Deep Learning Method

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