Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers

Ahamed, B. Shamreen; Arya, Meenakshi S.; Sangeetha, S.; Osvin, Nancy V. Auxilia

doi:10.1155/2022/7899364

Cited by 12 publications

(3 citation statements)

References 30 publications

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“…The results of the study are promising, but it is important to note that the study was conducted on a dataset of women with diabetes from Public Health Institute database. B. Shamreen Ahamed et al [ 20 ] used different classifiers like Random Forest, Light Gradient Boosting Machine (LGBM), Gradient Boosting Machine, Support Vector Machine (SVM), Decision Tree, and XGBoost, and the database used in this study was the PIMA data base from the UCI repository, with 768 instances. The study aimed to improve the detection accuracy with a percentage of 95.20 using the LGBM Classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance

Chellappan

Rajaguru

2023

Diagnostics

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Diabetes is a life-threatening, non-communicable disease. Diabetes mellitus is a prevalent chronic disease with a significant global impact. The timely detection of diabetes in patients is necessary for an effective treatment. The primary objective of this study is to propose a novel approach for identifying type II diabetes mellitus using microarray gene data. Specifically, our research focuses on the performance enhancement of methods for detecting diabetes. Four different Dimensionality Reduction techniques, Detrend Fluctuation Analysis (DFA), the Chi-square probability density function (Chi2pdf), the Firefly algorithm, and Cuckoo Search, are used to reduce high dimensional data. Metaheuristic algorithms like Particle Swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Seven classifiers, Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), and Support Vector Machine—Radial Basis Function (SVM-RBF), are utilized to classify the diabetic and non-diabetic classes. The classifiers’ performances are analyzed through parameters such as accuracy, recall, precision, F1 score, error rate, Matthews Correlation Coefficient (MCC), Jaccard metric, and kappa. The SVM (RBF) classifier with the Chi2pdf Dimensionality Reduction technique with a PSO feature selection method attained a high accuracy of 91% with a Kappa of 0.7961, outperforming all of the other classifiers.

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Section: Literature Reviewmentioning

confidence: 99%

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance

Chellappan

Rajaguru

2023

Diagnostics

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“…The results of the study are promising, but it is important to note that the study was conducted on a dataset of women with diabetes. B. Shamreen Ahamed et al, [10] analysis the different classifiers were used in the study: The classification models utilized in the study encompassed Random Forest, Light Gradient Boosting Machine (LGBM), Gradient Boosting Machine, Support Vector Machine (SVM), Decision Tree, and XGBoost. The primary objective of the research was to enhance accuracy, with the LGBM Classifier achieving a notable 95.20% accuracy.…”

Section: Objectivesmentioning

confidence: 99%

Detection of Diabetic through Micro Array Genes with enhancement of Classifiers Performance

Chellappan¹,

Rajaguru²

2023

Preprint

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Diabetes becomes a life threatening non-communicable disease in the world, according to International Diabetic Federation (IDF) in 2023, an estimated 537 million adults (20-79 years) are living with diabetes, which is equivalent to 9.3% of the global adult population. This number is predicted to rise to 643 million by 2030 and 783 million by 2045. Over 3 in 4 adults with diabetes live in low- and middle-income countries. Diabetes is a persistent metabolic condition marked by increased levels of glucose in the bloodstream. It is a significant global health concern, affecting millions of individuals worldwide. It having the symptoms of drowsiness for the whole day if not properly examined or treated well. It is targeted to spread over the younger age community and the number is growing in the exponential fashion. Even day to day many advancements have come from the researcher to diagnose, to find solutions to prevent from newer entry. Authors have taken a dataset with 57 non diabetic and 20 diabetic patients with the total 28735 micro array gene to undergone pre-processing process and reduced up to 22960 gene data using Dimensionality Reduction (DR) such as Detrend Fluctuation Analysis (DFA), Chi square probability density function (Chi2PDF), Firefly algorithm Cuckoo search were used in this research. Meta heuristic algorithms like Particle swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Further seven classification techniques such as Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), Support Vector Machine – Radial Basis Function (SVM-RBF) are using to make a decision, predictive analysis and segregate the data according to the level of blood glucose as Diabetic Patient (DP) and Non-Diabetic Patient (NDP).

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“…1.Gradient Boosting (G-Boost): G-Boost creates a stage-wise model and generalises the model by allowing for the optimization of an arbitrary differentiable loss function.Gradient boosting combines weak learners into a single strong learner in an iterative fashion. As each weak learner is added, a new model is fitted to provide a more accurate estimate of the response variable[52,53].…”

mentioning

confidence: 99%

DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath

Kapur,

Kumar,

Sharma

et al. 2023

JCM

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Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used clinical breath samples from 100 patients at a nationally recognized hospital to form the dataset. Data were then processed using a gradient boosting classifier model, and the performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, indicating an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. This has the potential to increase patient adherence to regular monitoring.

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Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers

Cited by 12 publications

References 30 publications

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance

Detection of Diabetic through Micro Array Genes with enhancement of Classifiers Performance

DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath

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