Prediction of Type-2 Diabetes Mellitus Disease Using Machine Learning Classifiers and Techniques

Ahamed, B. Shamreen; Arya, Meenakshi S.; Nancy, Auxilia Osvin

doi:10.3389/fcomp.2022.835242

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…The framework focused on the advantages of using multiple ML techniques rather than the single one in gaining the best accuracy levels. According to Ahamed et al [19], Light Gradient Boost Machine (LGBM) was the best performed algorithm with accuracy of 95.2 % comparing to other the ML algorithms while developing a diabetes prediction framework. Additionally, researchers concentrated on using one evaluation matrix only and choosing diabetes mellitus on type 2 for prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

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Predict Diabetes Using Voting Classifier and Hyper Tuning Technique

Kamal

Atiyah

2023

KJAR

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Today, diabetes is one of the most common chronic diseases in the world due to the people’s sedentary lifestyle which led to many health issues like heart attack, kidney frailer and blindness. Additionally, most of the people are unrealizable about the early-stage diabetes symptoms to prevent it. The above reasons were encouraging to develop a diabetes prediction system using machine learning techniques. The Pima Indian Diabetes Dataset (PIDD) was utilized for this framework as it is common and appropriate dataset in .CSV format. While there were not any duplicate or null values, however, some zero values were replaced, four outlier records were removed and data standardization were performed in the dataset. In addition, this project methodology divided into two phases of model selection. In the first phase, two different hyper parameter techniques (Randomized Search and TPOT(autoML)) were used to increase the accuracy level for each algorithm. Then six different algorithms (Logistic Regression, Decision Tree, Random Forest, K-nearest neighbor, Support Vector Machine and Naïve Bayes) were applied. In the second phase, the four best performed algorithms (with best estimated parameters for each of them) were chosen and used as an input for the voting classifier, because it applies to find the best algorithm between a group of multiple options. The result was satisfying, and Random Forest was achieved 98.69% in second stage, while its accuracy level was 81.04% in the previous one and it utilized to predict diabetes via a simple graphic user interface.

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Section: Literature Reviewmentioning

confidence: 99%

“…Due to its effectiveness, this algorithm was utilized in many prediction diseases such as diabetes, cancer, as so on. [16][17][18][19][20].…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Predict Diabetes Using Voting Classifier and Hyper Tuning Technique

Kamal

Atiyah

2023

KJAR

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“…The research demonstrates that the accuracy of the ensemble voting classifier on the Pima Indian diabetes dataset is 81%, outperforming other traditional predictive models. In this paper [10], the classifiers taken are logistic regression, XGBoost, gradient boosting, decision trees, ExtraTrees, random forest, and light gradient boosting machine (LGBM). The results obtained from these classifiers show that the LGBM classifier has the highest accuracy of 95.20% in comparison with the other algorithms.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Model for The Classification of Diabetes and Diabetes_Types Using Ensemble Approaches

Jaiswal

Gupta

Prasad

et al. 2023

JAIT

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Diabetes is a hereditary disorder that interferes with human life at all ages. It is challenging for cells to absorb glucose from the bloodstream when an individual has diabetes. The two main subtypes of diabetes are type 1 diabetes and type 2 diabetes. Type 1 diabetes develops when the pancreas cannot make enough insulin, whereas type 2 diabetes spreads due to insulin resistance. Diabetes is a recurrent, and chronic illness that is incurable. In modern healthcare systems, disease detection technology is pervasive. Detecting diabetes in its early stages is crucial for initiating timely treatment and halting disease progression. The proposed method has the potential not only to forecast the likelihood of future diabetes onset but also to identify the specific type of diabetes a person may develop. This paper investigates a potential solution for a diabetes prediction model in light of the continually rising prevalence of diabetes among patients. The proposed framework is designed using two datasets: the Pima Indian dataset, which is used to forecast diabetes, and the Diabetes Type dataset, which is used to identify the type of diabetes mellitus an individual has. This research aims to apply machine learning classifiers and ensemble models, such as Bagging, Voting, Averaging, and Stacking, for diabetes prediction. In this context, SMOTE (Synthetic Minority Oversampling Technique) and hyperparameter adjustment of the algorithms are considered and have substantially improved the findings. The developed heterogeneous ensemble model offers enhanced prediction rates with different performance criteria. Using the bagging technique, Random Forest attains a 96% accuracy rate, resulting in better predictions in the PID dataset. Regarding the Diabetes Type dataset, the Voting Ensemble Model provides a 98.5% accuracy rate. This study highlights that Ensemble learning models are effective in predicting diabetes and can outperform earlier relevant studies.

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“…Te DT considered are taken as a base_row sampling technique and column sampling technique. Te quantity of base learners is improved depending on the inputs and the variance is reduced to increase the accuracy [29]. It is taken into account as one of the important bagging methodologies.…”

Section: Random_forest (Rf)mentioning

confidence: 99%

Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers

Ahamed

Arya

Sangeetha

et al. 2022

Applied Computational Intelligence and Soft Computing

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The Diabetes-Mellitus (DM) disease is considered a persistent ailment that is triggered by excessive sugar levels in the blood of a person. It gives rise to severe health complications when left untreated and can also give rise to related diseases such as cardiac attack, nervous damage, foot problems, liver and kidney damage, and eye problems. These problems are caused by a series of factors interrelated to one another such as age, gender, family history, BMI, and Blood Glucose. Various Machine-Learning (ML) algorithms are being used in order to predict and detect the disease to avoid further complications of health. The Diabetes prediction process can be further improvised by identifying the type a person is being affected by and the probability of the occurrence of the related diseases. In order to perform the mentioned task, two types of the dataset are used in the study, namely, PIMA and a clinical survey dataset. Various ML algorithms such as Random Forest, Light Gradient Boosting Machine, Gradient Boosting Machine, Support Vector Machine, Decision Tree, and XGBoost are being used. The performance metrics used are accuracy, precision, recall, specificity, and sensitivity. Techniques such as Data Augmentation and Sampling are used. In comparison with the research conducted previously, the paper focuses on improvisation of the accuracy with a percentage of 95.20 using the LGBM Classifier, and Diabetes is also classified as Prediabetes or Diabetes using many Classification mechanisms.

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Prediction of Type-2 Diabetes Mellitus Disease Using Machine Learning Classifiers and Techniques

Cited by 10 publications

References 23 publications

Predict Diabetes Using Voting Classifier and Hyper Tuning Technique

Predict Diabetes Using Voting Classifier and Hyper Tuning Technique

An Empirical Model for The Classification of Diabetes and Diabetes_Types Using Ensemble Approaches

Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers

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