Predicting serious diabetic complications using hidden pattern detection

Farzi, Saeed; Kianian, Sahar; Rastkhadive, Ilnaz

doi:10.1109/kbei.2017.8324885

Cited by 6 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The process of Data analysis is done using python programming which is focused more on detecting diabetic diseases such as heart diseases and diabetic kidney related complications. Fig [1] shows the flow chart of the process in predicting heart and kidney risks in diabetic patients. Primarily, a data bank was created by collecting the blood parameters of around 140 patients from the laboratory with the permission of legal authorities.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of diabetic diseases using data analysis

Prabakar¹,

Shadaksharappa²,

Ramkumar³

2022

ijhs

View full text Add to dashboard Cite

Diabetes Mellitus is a most common metabolic disease globally. According to World health organization diabetes is one of the leading causes of deaths in world, and with an estimate of 415 million people suffering from it. According to the studies it is said that by the year 2030 India will have around 1 million patients suffering from Type-II diabetes. Diabetes has several complications related to itself .The impact of the diabetes over the various organs such as heart, kidney, eyes, peripheral nerves, blood vessels leads to coronary heart disease, ischemic problems, renal diseases, diabetic foot, and nerve damage. Diabetic complications such as heart and strokes account for 65% of diabetic deaths. More than 30% of the diabetic population suffers from diabetic nephropathy. Prediction of these diabetic complications at an earlier stage would help with the disease management, prevention of prognosis of the disease and help reduce the economic burden. This can be done using the data analysis method. In this paper a novel method to find out the diabetic diseases using the blood parameters of the patients such as HbA1C, serum cholesterol, serum albumin and others is done.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Saeed Farzi et.al [1], in the year 2017 proposed a method in the paper titled "Predicting serious diabetic complications using hidden pattern detection". In this paper they have predicted the complications of type-2 diabetes such as heart diseases, diabetic retinopathy, and diabetic kidney diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of diabetic diseases using data analysis

Prabakar¹,

Shadaksharappa²,

Ramkumar³

2022

ijhs

View full text Add to dashboard Cite

show abstract

“…The combination of risk factors is the key to improving the predictability of the data-based models. Most of the data-based models have in common the use of gender, age, duration of diabetes, prior amputation or ulceration, socio-economic and medical histories, and clinical and laboratory exams as input variables (25)(26)(27) .…”

Section: Literature Reviewmentioning

confidence: 99%

“…With the aim of predicting diabetic foot, Farzi et al (2017) (25) focused on patients with type 2 diabetes. They experimented with various types of supervised classification architectures, and they presented a comparative analysis for each of the input variables considered in that study (patient's medical history, infection year, blood pressure, blood and urine test results, referral date, and treatment process, as well as observed complications).…”

Section: Literature Reviewmentioning

confidence: 99%

Competitive neural layer-based method to identify people with high risk for diabetic foot

Ferreira

Oliveira

et al. 2020

Computers in Biology and Medicine

View full text Add to dashboard Cite

Background and Objective: To automatically identify patients with diabetes mellitus (DM) who have high risk of developing diabetic foot, via an unsupervised machine learning technique. Methods: We collected a new database containing 54 known risk factors from 250 patients diagnosed with diabetes mellitus. The database also contained a separate validation cohort composed of 73 subjects, where the perceived risk was annotated by expert nurses. A competitive neuron layer-based method was used to automatically split training data into two risk groups. Results: We found that one of the groups was composed of patients with higher risk of developing diabetic foot. The dominant variables that described group membership via our method agreed with the findings from other studies, and indicated a greater risk for developing such a condition. Our method was validated on the available test data, reaching 71% sensitivity, 100% specificity, and 90% accuracy. Conclusions: Unsupervised learning may be deployed to screen patients with diabetes mellitus, pointing out high-risk individuals who require priority follow-up in the prevention of diabetic foot with very high accuracy. The proposed method is automatic and does not require clinical examinations to perform risk assessment, being solely based on the information of a questionnaire answered by patients. 2 Our study found that discriminant variables for predicting risk group membership are highly correlated with expert opinion.

show abstract

“…Further this model was applied for two other relevant datasets. Farzi et al [9] focused their work on predicting complications of diabetes. The dataset chosen is mainly to predict complications of type-2 diabetes.…”

Section: Literature Surveymentioning

confidence: 99%

Diabetes Correlated Renal Fault Prediction through Deep Learning

Reddy¹,

Sethi²,

Rajender³

2020

EAI Endorsed Transactions on Pervasive Health and Technology

View full text Add to dashboard Cite

INTRODUCTION: Diabetic nephropathy is one of the complications of diabetes that causes damage to kidneys. Deep learning techniques are widely used to predict different diseases. OBJECTIVES: The main aim of this work is to develop an effective prediction model using deep learning. To get an effective model, a suitable dataset is considered that comprises of features related to diabetic nephropathy. METHODS: Deep belief network (DBN) is the proposed deep learning technique which is compared with naive bayes, CART decision tree, logistic regression and support vector machine. DBN is composed of Restricted Boltzmann Machines (RBM). The algorithms are analysed based on evaluation measures like area under PR curve, area under ROC curve, gini coefficient and jaccard index. RESULTS: After comparison of all algorithms, it was observed that DBN has performed better in terms of AUROC, gini coefficient and jaccard index with values 0.8203, 0.6406 and 0.7777 respectively. But CART obtained better value of 0.9039 only for AUPR. CONCLUSION: The proposed technique has outperformed other techniques in terms of three metrics and is identified as the best performing algorithm. Hence, it is suggested to use DBN while predicting diabetic nephropathy.

show abstract

Predicting serious diabetic complications using hidden pattern detection

Cited by 6 publications

References 16 publications

Prediction of diabetic diseases using data analysis

Prediction of diabetic diseases using data analysis

Competitive neural layer-based method to identify people with high risk for diabetic foot

Diabetes Correlated Renal Fault Prediction through Deep Learning

Contact Info

Product

Resources

About