Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations

Abstract

One of the capacious applications of data science could be its use in bioinformatics. With its proper implementation, chronic diseases like diabetes mellitus, responsible for millions of deaths worldwide, could be diagnosed and predicted with high efficacy. But if not attended, could lead to fatal issues such as kidney failures, heart diseases, and even limb amputation. Diabetic cases have only elevated in numbers in the recent past. The authors use various machine learning, deep learning, and data dimensionality reduction techniques to detect diabetes mellitus. The research is principally conducted on two datasets, first from the Frankfurt Hospital, Germany, second from the University of California, Irvine repository. Models such as support vector machines, Naïve Bayes, and Random Forests were implemented to classify diabetic patients from non‐diabetic ones. Subsequently, after hyperparameter tuning, a comparative study on the results was done and the most prominent model was promoted. This process was repeated for the datasets with reduced dimensionality using linear discriminant analysis and principal component analysis. For the Frankfurt, Germany, dataset, K‐nearest neighbours showed the best accuracy of 98.2%, and the Random Forest classifier for the University of California, Irvine, repository showed 99.2%. With such proficiency, the authors thereby propose a statistical approach for the prediction of diabetes in its early stages. They hope to counter the concern of undiagnosed diabetic cases in developing nations where there is a lack of a basic healthcare system.

Diabetes Prediction and Classification Using Self-adaptive Evolutionary Algorithm with Convolutional Neural Network

“…In comparison analysis, the proposed SAEA with CNN approach is estimated using three benchmark datasets such as PIDD, Frankfurt Hospital, Germany and NCSU datasets achieved better values for the metrics. The proposed method achieved the 99.87%, 99.82%, 99.90%, 99.86% and 99.99%, 99.99%, 99.99%, 99.99% for accuracy, precision, recall, F1-score by using PIDD and Frankfurt Hospital, Germany datasets when compared to the existing methods of ML and DL approaches such as DNN [16], ANN [17], SAE-CNN [18], Deep 1D-CNN [19], ResNet18 and ResNet50-ReliefF [20] and DCNN with data modelling [21] respectively. These results show that the proposed method achieves better results when compared to the existing methods.…”

“…Mahendra Kumar Gourisaria [17] developed the ML approaches for the diagnosis and prediction of diabetes mellitus. The suggested approach was predominantly considered the two-benchmark dataset.…”

“…Table 4 compares the proposed SAEA with CNN method to a collection of existing algorithms based on various evaluation metrics. The existing methods such as [16][17][18][19] are compared by using accuracy, precision, recall, and F1-score respectively.…”

Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies

“…Decision Tree [47] categorizes the leveled trained data into rules or trees [48]. It is a technique for approximating discrete-valued functions that is powerful with noisy data, and the learned function is constituted by a decision tree.…”

Modulating sparks in a pulse train for repetitive and energy efficient plasma generation

“…Machine learning (ML) has exhibited significant potential for addressing problems in complex systems. The concept of Artificial Intelligence for Science has been proposed and implemented in gas dynamics [25,26], combustion [27,28], disease diagnosis [29,30] and protein structure prediction [31,32]. ML has also been used in plasma physics and applications, and some pioneering studies in the plasma science community can be found in refs.…”