Classification of Diabetic Patient Data Using Machine Learning Techniques

Singh, Pankaj Pratap; Prasad, Shitala; Das, Bhaskarjyoti; Poddar, Upasana; Choudhury, D.

doi:10.1007/978-981-10-7386-1_37

Cited by 10 publications

(4 citation statements)

References 2 publications

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“…In this paper, a framework is presented which shows the different flood hazards in spatial hotspots areas and also assessed the vulnerability in the districts using MODIS data [16]. A ML approach was used to classify and find risk based on diabetics disease [17].…”

Section: Related Workmentioning

confidence: 99%

A Novel Framework for Risk Prediction in the Health Insurance Sector using GIS and Machine Learning

Baruah,

Singh,

Ojah

2023

IJACSA

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Evaluation of risk is a key component to categorize the customers of the life insurance businesses. The underwriting technique is carried out by the industries to charge the policies appropriately. Due to the availability of data hugely, the automation of underwriting process can be done using data analytics technology. Due to this, the underwriting process becomes faster and therefore quickly processes a large number of applications. This study is carried to enhance risk assessment of the applicants of life insurance industries using predictive analytics. In this research, the Geographical Information Systems (GIS) system is used to collect the data such as Air pollution, Industrial area, Covid-19 and Malaria of various geographic areas of our country, since these factors attribute to the risk of an applicant of life insurance business. Thereafter, the research is carried out using this dataset along with another dataset containing more than 50,000 entries of normal attributes of applicants of a life insurance company. Artificial Neural Network (ANN), Decision Tree (DT), and Random forest (RF) algorithms are applied on both the datasets to predict the risks of the applicants. The results showed that random forest outperformed among all the algorithms, providing the more accurate result.

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Section: Related Workmentioning

confidence: 99%

A Novel Framework for Risk Prediction in the Health Insurance Sector using GIS and Machine Learning

Baruah,

Singh,

Ojah

2023

IJACSA

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“…In [11] In [12] they suggest implementing machine learning approaches to classify diabetes medical data. The objective of this work is to discover insights by observing the trends in the repository and using predictive analytics to classify these diseases.…”

Section: In [5] They Have Explored Implementing Machinementioning

confidence: 99%

A detailed survey on Prognostication of diabetes diagnosis on the basis of machine learning techniques and the detection approaches to diabetic retinopathy using Artificial Intelligence

2021

IJATCSE

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The aim of the study is to compare, assess the optimum tools as well as the techniques and advanced features focused on prediction of diabetes diagnosis based on machine learning tactics and diabetic retinopathy using Artificial Intelligence. The literature on data science, Artificial Intelligence (AI) contains important knowledge and understanding of AI entities such as Data science, machine learning, deep learning, Medical image processing, feature extraction, classification techniques, etc. Diabetes diagnosis is a phenomenon that impacts individuals around the globe. Now, with diabetes impacting people from children to the elderly, the out-dated approaches to diabetes diagnosis should be replaced with new, time-saving technologies. There's several studies carried out by researchers to recognise and predict diabetes. Here plenty of classifiers in machine learning can be used, such as KNN, Random Tree, etc.They can save time and get more precise outcome when using these techniques to predict diabetes. Diabetic retinopathy (DR) is a typical disorder of diabetic disease that induces vision-impacting lesions in the retina. It also can turn to visual impairment if it is not addressed early. DR therapy only helps vision. Deep learning has in recent times being one of the most widely used approaches that has accomplished higher outcomes in so many fields, especially in the analysing and identification of medical image classification. In medical image processing, convolutional neural networks (CNN) using transfer learning are commonly used as a deep learning approach and they are incredibly beneficial. Key words: Diab

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“…This intelligent system is required to think of a variety of factors and recognize appropriate model between different constraints so that computational complexity, programming complications as well as the accuracy of diabetes classification can be enhanced. Derived from machine learning and an artificial network, many approaches have been tested on the diabetes dataset 15–17 . However, application to diabetes diagnosis remains a challenge for conceptual intelligence after such rapid development.…”

Section: Introductionmentioning

confidence: 99%

“…Derived from machine learning and an artificial network, many approaches have been tested on the diabetes dataset. [15][16][17] However, application to diabetes diagnosis remains a challenge for conceptual intelligence after such rapid development. Various machine learning classification techniques (such as K-Nearest Neighbors [KNN], Kernel SVM, Naïve Bayes [NB], Decision Tree [DT], Support Vector Machine [SVM], Logistic Regression [LR], Random Forest [RF], Xgboost), have been developed to classify the diabetes .…”

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confidence: 99%

Functional link convolutional neural network for the classification of diabetes mellitus

Jangir

Joshi

Kumar

et al. 2021

Numer Methods Biomed Eng

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Diabetes is a faction of metabolic ailments distinguished by hyperglycemia which is the consequence of a defect, in the action of insulin, insulin secretion, or both and producing various abnormalities in the human body. In recent years, the utilization of intelligent systems has been expanded in disease classification and numerous researches have been proposed. In this research article, a variant of Convolutional Neural Network (CNN) that is, Functional Link Convolutional Neural Network (FLCNN) is proposed for the diabetes classification. The main goal of this article is to find the potential of a computationally less complex deep learning network like FLCNN and applied the proposed technique on a real dataset of diabetes for classification. This article also presents the comparative studies where various other machine learning techniques are implemented and outcomes are compared with the proposed FLCNN network. The performance of each classification techniques have been evaluated based on standard measures and also validated with a non‐parametric statistical test such as Friedman. Data for modeling diabetes classification is collected from Bombay Medical Hall, Upper Bazar, Ranchi, India. Accuracy achieve by the proposed classifier is more than 90% which is closer to the other state‐of‐the‐art implemented classifiers.

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Classification of Diabetic Patient Data Using Machine Learning Techniques

Cited by 10 publications

References 2 publications

A Novel Framework for Risk Prediction in the Health Insurance Sector using GIS and Machine Learning

A Novel Framework for Risk Prediction in the Health Insurance Sector using GIS and Machine Learning

A detailed survey on Prognostication of diabetes diagnosis on the basis of machine learning techniques and the detection approaches to diabetic retinopathy using Artificial Intelligence

Functional link convolutional neural network for the classification of diabetes mellitus

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