article · Applied Sciences
Accurate prediction of diabetes helps lessen disease severity through timely intervention, yet diagnostic datasets often suffer from missing values, outliers, and trade-offs between precision and operational rules. To tackle these issues, a classification framework tailored to diabetes in pregnant women has been developed using the Pima Indians Diabetes Dataset. The approach begins with data preprocessing, which involves rejecting outliers, imputing missing values, standardising attributes, and selecting relevant features. A fuzzy k-nearest neighbours model is then modified using uncertainty theory to refine the membership function, with hyperparameters optimised via grid search. This tuned classifier addresses belief degrees and operational laws to reduce misclassifications. Testing shows the model outperforms standard k-nearest neighbours, fuzzy variants, naive Bayes, and decision tree classifiers, achieving an accuracy of 90.63 percent, specificity of 85.00 percent, precision of 93.18 percent, and an area under the curve of 94.13 percent.
Diabetes is a widespread chronic condition where early and precise detection is essential for effective treatment. Health datasets frequently feature missing entries and anomalies that impair automated diagnosis. By better handling uncertainty and incomplete records, this algorithm improves classification accuracy for pregnant women, supporting the development of more dependable computational tools to assist healthcare providers in identifying diabetes early.
This algorithm represents early-stage software research tested on a standard benchmark dataset rather than deployed in a live clinical environment. It could eventually support digital diagnostic tools and clinical decision-support software used by healthcare professionals screening pregnant women for diabetes. Further validation on diverse clinical datasets and integration into existing health software workflows are required before the model could reach commercial application.
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Diabetes, a metabolic disease in which the blood glucose level rises over time, is one of the most common chronic diseases at present. It is critical to accurately predict and classify diabetes to reduce the severity of the disease and treat it early. One of the difficulties that researchers face is that diabetes datasets are limited and contain outliers and missing data. Additionally, there is a trade-off between classification accuracy and operational law for detecting diabetes. In this paper, an algorithm for diabetes classification is proposed for pregnant women using the Pima Indians Diabetes Dataset (PIDD). First, a preprocessing step in the proposed algorithm includes outlier rejection, imputing missing values, the standardization process, and feature selection of the attributes, which enhance the dataset’s quality. Second, the classifier uses the fuzzy KNN method and modifies the membership function based on the uncertainty theory. Third, a grid search method is applied to achieve the best values for tuning the fuzzy KNN method based on uncertainty membership, as there are hyperparameters that affect the performance of the proposed classifier. In turn, the proposed tuned fuzzy KNN based on uncertainty classifiers (TFKNN) deals with the belief degree, handles membership functions and operation law, and avoids making the wrong categorization. The proposed algorithm performs better than other classifiers that have been trained and evaluated, including KNN, fuzzy KNN, naïve Bayes (NB), and decision tree (DT). The results of different classifiers in an ensemble could significantly improve classification precision. The TFKNN has time complexity O(kn2d), and space complexity O(n2d). The TFKNN model has high performance and outperformed the others in all tests in terms of accuracy, specificity, precision, and average AUC, with values of 90.63, 85.00, 93.18, and 94.13, respectively. Additionally, results of empirical analysis of TFKNN compared to fuzzy KNN, KNN, NB, and DT demonstrate the global superiority of TFKNN in precision, accuracy, and specificity.
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DOI: 10.3390/app12030950
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