Nowadays there are lots of fatal diseases are growing at a rapid rate. We consider about four primary diseases like jaundice, diabetes mellitus, yellow fever, and cholera. In this paper, we design a novel method with the help of fuzzy and FPGA system for prediction multiple diseases in a rural area. Association rule mining technique helps to define fuzzy rules, which implemented on both Spartan3-E and Artix-7 FPGA kit. Due to this implementation, it is easier to design a cost-effective and portable system for multi-disease prediction. The innovation lies in design a low power FPGA and meticulousness method for identification, prediction of four fatal diseases. The whole plan has tested on Xilinx and Cadence tool for generating RTL model and Layout design.
ZhuJ., LiuA.A., ChenM., TasdizenT. and SuH., “Special Issue on Biomedical Big Data: Understanding, Learning and Applications,”, IEEE Transactions on Big Data3(4) (2017), 375–377.
2.
JovicA., KukoljaD., JozicK. and HorvatM., “A web platform for analysis of multivariate heterogeneous biomedical time-series — A preliminary report,”2016 International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, 2016, pp. 1–4.
3.
LeandrouS., PetroudiS., Reyes-AldasoroC.C., KyriacouP.A. and PattichisC.S., “Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer’s disease: A Methodological Review,”, IEEE Reviews in Biomedical EngineeringPP(99), 1–1.
4.
VenkatesanC., KarthigaikumarP., PaulA., SatheeskumaranS. and KumarR., “Ecg Signal Preprocessing And Svm Classifier Based Abnormality Detection In Remote Healthcare Applications,”, IEEE AccessPP(99), 1–1.
5.
Mark,Patrick B.,
WinocourPeter, and DayCaroline.
“Management of lipids in adults with diabetes mellitus and nephropathy and/or chronic kidney disease: Summary of joint guidance from the Association of British Clinical Diabetologists (ABCD) and the Renal Association (RA).”, British Journal of Diabetes17.2 (2017), 64–72.
6.
ManeV.M,
JadhavD V,
ShirbahadurkarS D, “Hybrid classifier and region-dependent integrated features for detection of diabetic retinopathy”, Journal of Intelligent & Fuzzy Systems32(4) (2017), 2837–2845.
7.
SunX., LuZ., ZhangX., SalathéM. and
CaoG., “Infectious Disease Containment Based on a Wireless Sensor System,”, IEEE Access4 (2016), 1558–1569.
8.
ZadehL.A., “Fuzzy logic computing with words,”, IEEE Transactions on Fuzzy Systems4(2) (1996), 103–111.
9.
ZahediK.A,
SallehK. A,
RazakA, “Application of a preference relationship in decision-making based on intuitionistic fuzzy soft sets”, Journal of Intelligent & Fuzzy Systems34(1) (2018), 123–139.
10.
RoyChowdhuryS.,
ChakrabartiD.,
SahaH., “FPGA realization of a Smart Processing System for Clinical Diagnostic Applications using Pipelined Datapath Architectures”, Microprocessors and Microsystems32(2) (2008), 107–120.
11.
Roy ChowdhuryS.,
ChakrabartiD.SahaH., “Development of an FPGA based smart diagnostic system for Spirometric Data Processing Applications”, International Journal of Smart Sensing and Intelligent Systems1(4) (2008), 985–1018.
12.
Roy ChowdhuryS. and
SahaH., “Development of an FPGA based Fuzzy Neural Network System for early diagnosis of critical health condition of a patient”, Computers in Biology and Medicine40(2) (2010), 190–200.
13.
Roy ChowdhuryS.,
RoyA. and SahaH., “ASIC design of a digital fuzzy system on chip for medical diagnostic applications”, Journal of Medical Systems35(2) (2011), 221–235.
14.
DasS., Roy ChowdhuryS. and
SahaH., “Accuracy enhancement in a fuzzy expert decision making system through optimization of membership functions and its application in a medical diagnostic decision making system”, Journal of Medical Systems, Journal of Medical Systems36(3) (2012), 1607–1620.
15.
DeoMadhav G,
Doctor population ratio for India – The reality, Indian Journal of Medical Research137(4) (2013), 632–635.
Hong,Tzung-Pei,
LinKuei-Ying and
WangShyue-Liang,
“Fuzzy data mining for interesting generalized association rules.”, Fuzzy sets and systems138.2 (2003), 255–269.
19.
MitraS. and HayashiY., “Neuro-fuzzy rule generation: Survey in soft computing framework,”, IEEE Transactions on Neural Networks11(3) (2000), 748–768.
