Mining of multiple ailments correlated to diabetes mellitus

Efficient and user friendly database technologies have enabled the digitization of information pertaining to the medical domain. This has not only eased the smooth record manipulation but also attracted man a researchers to explore certain challenges to solve through implementation of data mining tools and techniques. Among the nature of ailments, the information related to diabetes mellitus (DM) are found to be the maximally digitized. This has provided a challenging but buzzing platform for the researchers to do in-depth analysis and present modern edge solutions which can lead to early diagnosis of the fatal ailment. There arise numerous side-effects to a human body when it is affected by DM. These multiple ailments attack a human body with the direct or indirect influence of DM and it’s corresponding drug intake. Thus, there has been a demand for a generic scheme which can predict the likeliness of certain multiple ailments that a DM patient is supposed to be attacked by in near future. In this work, a suitable scheme has been proposed in the same direction. This scheme provides a viable platform where the probabilities of multiple ailments for a DM patient can be computed. The proposed scheme also provides the probabilities of occurrence of individual ailment as well as the probabilities of occurrence of certain combination of the ailments. Occurrence of three of the major ailment are being computed in this work. These are retinal disorder, kidney malfunction, and heart disease. A Fuzzy logic strategy has been used for matching several disease constraints and produce a decisive outcome. Certain number of novel heuristic functions are presented which take these outputs and provide a probabilistically accurate prediction of occurrences of the said ailments. Suitable experimental evaluation have been made with proper data inputs. The proposed scheme has also been compared with competent schemes. An overall rates of accuracy of 97% is calculated based on a k-fold cross validation performance metric.

[1]  Y Ichioka,et al.  Parallel distributed processing model with local space-invariant interconnections and its optical architecture. , 1990, Applied optics.

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  E. Dereniak,et al.  Gaussian profile estimation in one dimension. , 2007, Applied optics.

[4]  D. J. Hamilton,et al.  Simulating and predicting blood glucose levels for improved diabetes healthcare , 2008 .

[5]  Xun Xu,et al.  Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images , 2017, IET Image Process..

[6]  Mehrbakhsh Nilashi,et al.  An analytical method for diseases prediction using machine learning techniques , 2017, Comput. Chem. Eng..

[7]  Hongwei Guo,et al.  A Simple Algorithm for Fitting a Gaussian Function [DSP Tips and Tricks] , 2011, IEEE Signal Processing Magazine.

[8]  N. Sneha,et al.  Analysis of diabetes mellitus for early prediction using optimal features selection , 2019, Journal of Big Data.

[9]  J. Coresh,et al.  Prevalence of chronic kidney disease in the United States. , 2007, JAMA.

[10]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[11]  F. Hu,et al.  Associations of diet with albuminuria and kidney function decline. , 2010, Clinical journal of the American Society of Nephrology : CJASN.

[12]  B. Dhomse Kanchan,et al.  Study of machine learning algorithms for special disease prediction using principal of component analysis , 2016, 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC).

[13]  M. Morin,et al.  Propagation of super-Gaussian field distributions , 1992 .

[14]  Min Chen,et al.  Disease Prediction by Machine Learning Over Big Data From Healthcare Communities , 2017, IEEE Access.

[15]  G. M. Nasira,et al.  Prediction of Heart Diseases and Cancer in Diabetic Patients Using Data Mining Techniques , 2015 .

[16]  Gajendra P. S. Raghava,et al.  BMC Bioinformatics BioMed Central Methodology article Machine learning techniques in disease forecasting: a case study on rice blast prediction , 2006 .

[17]  Shahebaz Ahmed Khan,et al.  Co- Disease Prediction using Multileyer Perceptron and Classification from Diabetic Medical Data Sets , 2018, International Journal of Engineering & Technology.

[18]  Thippa Reddy Gadekallu,et al.  An Efficient Attribute Reduction and Fuzzy Logic Classifier for Heart Disease and Diabetes Prediction , 2018 .

[19]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[20]  S. Edward Rajan,et al.  Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images , 2014, IET Image Process..

[21]  Hongwei Guo,et al.  A Simple Algorithm for Fitting a Gaussian Function , 2012 .

[22]  Yeshvendra K. Singh,et al.  Heart Disease Prediction System Using Random Forest , 2016 .

[23]  Ruo-Ping Han,et al.  Disease prediction with different types of neural network classifiers , 2016, Telematics Informatics.

[24]  Dilip Singh Sisodia,et al.  Prediction of Diabetes using Classification Algorithms , 2018 .