Analysis of Diabetes mellitus using Machine Learning Techniques

Bhat, Salliah Shafi; Selvam, Venkatesan; Ansari, Gufran Ahmad; Ansari, Mohd Dilshad

doi:10.1109/impact55510.2022.10029058

Cited by 8 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is followed by the split of training and test data in which training data consists of 80% of the actual data while test data has the remaining balance of 20%. The 80% training data and 20% test data split ratio is commonly applied in the diabetes domain [ 48 ]. Next, the Isolation Forest is employed for outlier detection and removal in the data.…”

Section: Methodsmentioning

confidence: 99%

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Idris,

Ismail,

Jaya

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

show abstract

Section: Methodsmentioning

confidence: 99%

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Idris,

Ismail,

Jaya

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Being a linear Support Vector Machine (SVM), SVC's decision boundary is a hyperplane defined by: 𝑓(𝑥) = 𝑤 𝑇 𝑥 + 𝑏 (1) Where 𝑤 is the weight vector (coefficients) that indicates the importance of each feature. In essence, the weightage of the coefficients in the decision function provided an intuitive representation of the relative importance of each feature [11].…”

Section: Determining Feature Importancementioning

confidence: 99%

Comparative analysis of machine learning algorithms for Diabetes Mellitus prediction

2024

ACE

View full text Add to dashboard Cite

Type II Diabetes Mellitus (T2DM) has become an increasingly prevalent disease due to the rising number of the obese population. Even though this disorder is the leading cause of more severe health complications, studies have shown that T2DM is largely preventable if detected in an early phase. To improve current diagnosis and prognosis procedures, this study aims to examine three machine learning algorithmsK-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Classification (SVC)and their potential in making accurate predictions on the outcomes of the Pima Indians Diabetes Dataset (PIDD). After training and 5-fold cross validation, the results show that the RF algorithm has the highest accuracy at 75.25%, followed by SVC at 74.91% and KNN at 71.01%. In addition, feature importances were evaluated for all three models, yet we observed a drastic difference in the top-ranked features across different models, which implies that more training and larger datasets are necessary before realizing these computational approaches into practice. Nevertheless, the potential of these approaches highlighted in this study demonstrated that machine learning is a burgeoning strategy in clinical use and in solving real-world problems.

show abstract

An IoT Framework to Support Rural Population with Diabetic Related Issues via Optimization Algorithms

Gunjan,

Shaik,

Pathak

2023

Internet of Things

View full text Add to dashboard Cite

Analysis of Diabetes mellitus using Machine Learning Techniques

Cited by 8 publications

References 19 publications

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Comparative analysis of machine learning algorithms for Diabetes Mellitus prediction

An IoT Framework to Support Rural Population with Diabetic Related Issues via Optimization Algorithms

Contact Info

Product

Resources

About