Detection and prediction of diabetes using effective biomarkers

ABSTRACTDiabetes is a prevalent and costly condition, with early diagnosis pivotal in mitigating ‎its progression and complications. The diagnostic process often contends with data ‎ambiguity and decision uncertainty, adding complexity to achieving definitive ‎outcomes. This study addresses the diabetes diagnostic challenge through data mining ‎and machine learning techniques. It involves training various machine learning ‎algorithms and conducting statistical analysis on a dataset comprising 520 patients, ‎encompassing both normal and diabetic cases, to discern influential features.‎ Incorporating 17 features as classifier inputs, this research evaluates the diagnostic ‎performance using four reputable techniques: support vector machine (SVM), random ‎forest (RF), multi-layer perceptron (MLP), and k-nearest neighbor (kNN). The outcomes ‎underscore the SVM model's superior performance, boasting accuracy, specificity, and ‎sensitivity values of 98.78±1.96%, 99.28±1.63%, and 97.32±2.45%, ‎respectively, across 50 iterations. The findings establish SVM as the preferred method ‎for diabetes diagnosis.‎ This study highlights the efficacy of data mining and machine learning models in ‎diabetes diagnosis. While these methods exhibit respectable predictive accuracy, their ‎integration with a physician's assessment promises even better patient outcomes.‎KEYWORDS: Data miningdiabetesSVMdetectionprediction Abbreviations ANN=Artificial Neural NetworkAUC=Area under CurveCDC=Centers for Disease ControlCPCSSN=Canadian Primary Care Sentinel Surveillance NetworkDT=Decision TreeFN=False NegativeFP=False PositivekNN=k Nearest NeighborLDA=Linear Discrimination AnalysisLR=Logistic RegressionML=Machine LearningMLP=Multi-Layer PerceptronNB=Naive BayesianPIDD=Pima Indians Diabetes DatasetRF=Random ForestROC=Receiver Operating CharacteristicSVM=Support Vector MachineTN=True NegativeTP=True PositiveUKPDS=UK Prospective Diabetes StudyDisclosure statementNo potential conflict of interest was reported by the author(s)Authors’ contributionsAll authors evenly contributed to the whole work. All authors read and approved the final manuscript.Availability of data and materialsThe data used in this paper is cited throughout the paper.Ethical approvalThis article does not contain any studies with human participants performed by any of the authors.Additional informationFundingNo source of funding for this work.Notes on contributorsMohammad Ehsan FarnoodianMohammad Ehsan Farnoodian received a B.S. degree in biomedical engineering-‎‎bioelectric from Tehran Medical Science, Islamic Azad University, Tehran, Iran, ‎and earned his M.S. degree in biomedical engineering-bioelectric from Science and ‎Research branch, Islamic Azad University, Tehran, Iran, in 2023. He is passionately ‎dedicated to the examination and interpretation of biomedical data, particularly in ‎the context of disease prediction and detection. His academic pursuits involve in-‎depth exploration of biomedical data analysis intricacies, with a specific focus on ‎employing data-driven approaches for disease anticipation and identification.‎Mohammad Karimi MoridaniMohammad Karimi Moridani received a BS degree in electrical engineering-‎Electronic from 2006, and he obtained MS and Ph.D. degrees in biomedical ‎engineering-bioelectric in 2008 and 2015, respectively. Currently, he serves as an ‎assistant professor in the biomedical engineering department at Tehran Medical ‎Science, Islamic Azad University in Tehran, Iran. His research focuses on ‎biomedical signal and image processing, nonlinear time series analysis, and ‎cognitive science, with specific applications ranging from ECG, HRV, and EEG ‎signal processing for disease detection and prediction to epileptic seizure ‎prediction, pattern recognition, image processing for facial and beauty recognition, ‎watermarking, and more. He is driven by a passion to contribute meaningfully to ‎the scientific community and employs data-driven methodologies to address ‎critical challenges in healthcare and related fields.‎Hanieh MokhberHanieh Mokhber received a B.S. degree in biomedical engineering-bioelectric ‎from Islamic Azad University of Tehran Medical science. Her scholarly endeavors ‎involve a meticulous exploration of the complexities of biomedical data analysis, ‎with a specific and unwavering emphasis on harnessing data-driven methodologies ‎to anticipate and identify various diseases.‎