Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance
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 id...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/16/2654 |
| _version_ | 1797585028079681536 |
|---|---|
| author | Dinesh Chellappan Harikumar Rajaguru |
| author_facet | Dinesh Chellappan Harikumar Rajaguru |
| author_sort | Dinesh Chellappan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:59:58Z |
| format | Article |
| id | doaj.art-23c68f9768f54ea08107a0d70b725956 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T23:59:58Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-23c68f9768f54ea08107a0d70b7259562023-11-19T00:48:15ZengMDPI AGDiagnostics2075-44182023-08-011316265410.3390/diagnostics13162654Detection of Diabetes through Microarray Genes with Enhancement of Classifiers PerformanceDinesh Chellappan0Harikumar Rajaguru1Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore 641 407, Tamil Nadu, IndiaDepartment of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638 401, Tamil Nadu, IndiaDiabetes 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.https://www.mdpi.com/2075-4418/13/16/2654type II diabetes mellitusmachine learningpredictionDimensionality Reductionclassifiers |
| spellingShingle | Dinesh Chellappan Harikumar Rajaguru Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance Diagnostics type II diabetes mellitus machine learning prediction Dimensionality Reduction classifiers |
| title | Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance |
| title_full | Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance |
| title_fullStr | Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance |
| title_full_unstemmed | Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance |
| title_short | Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance |
| title_sort | detection of diabetes through microarray genes with enhancement of classifiers performance |
| topic | type II diabetes mellitus machine learning prediction Dimensionality Reduction classifiers |
| url | https://www.mdpi.com/2075-4418/13/16/2654 |
| work_keys_str_mv | AT dineshchellappan detectionofdiabetesthroughmicroarraygeneswithenhancementofclassifiersperformance AT harikumarrajaguru detectionofdiabetesthroughmicroarraygeneswithenhancementofclassifiersperformance |