Pima Indian Diabetes Dataset Research Articles

Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture

Diabetes is a deadly chronic disease that occurs when the pancreas is not able to produce ample insulin or when the body cannot use insulin effectively. If undetected, it may lead to a host of health complications. Hence, accurate and explainable early-stage detection of diabetes is essential for the proper administration of treatment options in leading a healthy and productive life. For this, we developed an interpretable TabNet model tuned via Bayesian optimization (BO). To achieve model-specific interpretability, the attention mechanism of TabNet architecture was used, which offered the local and global model explanations on the influence of the attributes on the outcomes. The model was further explained locally and globally using more robust model-agnostic LIME and SHAP eXplainable Artificial Intelligence (XAI) tools. The proposed model outperformed all benchmarked models by obtaining high accuracy of 92.2% and 99.4% using the Pima Indians diabetes dataset (PIDD) and the early-stage diabetes risk prediction dataset (ESDRPD), respectively. Based on the XAI results, it was clear that the most influential attribute for diabetes classification using PIDD and ESDRPD were Insulin and Polyuria, respectively. The feature importance values registered for insulin was 0.301 (PIDD) and for polyuria 0.206 was registered (ESDRPD). The high accuracy and ancillary interpretability of our objective model is expected to increase end-users trust and confidence in early-stage detection of diabetes.

An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI.

Diabetes is a chronic disease that continues to be a primary and worldwide health concern since the health of the entire population has been affected by it. Over the years, many academics have attempted to develop a reliable diabetes prediction model using machine learning (ML) algorithms. However, these research investigations have had a minimal impact on clinical practice as the current studies focus mainly on improving the performance of complicated ML models while ignoring their explainability to clinical situations. Therefore, the physicians find it difficult to understand these models and rarely trust them for clinical use. In this study, a carefully constructed, efficient, and interpretable diabetes detection method using an explainable AI has been proposed. The Pima Indian diabetes dataset was used, containing a total of 768 instances where 268 are diabetic, and 500 cases are non-diabetic with several diabetic attributes. Here, six machine learning algorithms (artificial neural network (ANN), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost, XGBoost) have been used along with an ensemble classifier to diagnose the diabetes disease. For each machine learning model, global and local explanations have been produced using the Shapley additive explanations (SHAP), which are represented in different types of graphs to help physicians in understanding the model predictions. The balanced accuracy of the developed weighted ensemble model was 90% with a F1 score of 89% using a five-fold cross-validation (CV). The median values were used for the imputation of the missing values and the synthetic minority oversampling technique (SMOTETomek) was used to balance the classes of the dataset. The proposed approach can improve the clinical understanding of a diabetes diagnosis and help in taking necessary action at the very early stages of the disease.

A proposed model for diabetes mellitus classification using coyote optimization algorithm and least squares support vector machine

One of the most dangerous health diseases affecting the world's population is diabetes mellitus (DM), and its diagnosis is the key to its treatment. Several methods have been implemented to diagnose diabetes patients. In this work, a hybrid model which combines of coyote optimization algorithm (COA) and least squares support vector machine (LS-SVM) is proposed to classify of Type-II-DM patients. LS-SVM classifier is applied for classification process but it's very sensitive when its parameter values are changed. To overcome this problem, COA algorithm is implemented to optimize parameters of the LS-SVM classifier. This is the goal of the proposed model called the COA-LS-SVM. The proposed model is implemented and evaluated using pima Indians diabetes dataset (PIDD). Also, it's compared with several classification algorithms that were implemented on the same PIDD. The experimental results demonstrated the effectiveness of the proposed model and its superiority over other algorithms, as it could accomplish an average classification accuracy of 98.811%.

A Comparative Analysis of Diabetes Prediction using Different Machine Learning Algorithms

The endocrine disorder diabetes is a condition where the body's glucose levels are abnormally high. Diabetes type II is highly prevalent among elderly people. Worldwide, this number is rising quickly. Furthermore, diabetes creates major health issues that might result in organ failure and paralysis in addition to lowering the blood glucose content. Additionally, it shortens the patients' lives [1]. Early diabetes classification involves seeing a patient at a diagnostic facility and consulting doctors, which is a very time consuming process. A mechanism has been created to deal with these significant problems. A classification of the patient's level of diabetes using machine learning (ML) algorithms has been addressed in this paper. Previous works considered only five different ML algorithms. We have extended and compared the classification of diabetes prediction using eight different ML algorithms. The database used to train the models is taken from the Pima Indian Diabetes datasets as available from the UCI ML repository [2]. Accuracy, Precision, recall, and F1 score are the four metrics that have been used to analyze and compare the performances of prediction. In comparison to other methods, simulation results indicate that the Neural Network model has the highest accuracy, at 93%. Another performance metric has been the receiver operating characteristics (RoC) that also shows that NN has the maximum area among all the eight algorithms. Simulation results show this area as 0.740.

Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture

Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days. To overcome such issues in e-Healthcare systems, Internet of Things (IoT) applications have been deployed using cloud computing (CC) approaches. There are still a number of disadvantages to using CC, including latency, bandwidth, energy usage, and security and privacy concerns. Fog computing (FC), a CC development, may be able to overcome these obstacles. DiaFog enabling remote users for real-time diagnosis of diabetic mellitus disease (DMD) has been proposed in this study, which is based on the combined ideas of IoT, cloud, and fog computing, as well as an ensemble deep learning (EDL) technique. The proposed system is trained with EDL approaches on the integrated dataset of two diabetes mellitus disease datasets (DMDDs), namely, Pima Indians Diabetes Dataset (PIDD) and Hospital Frankfurt Germany Diabetes Dataset (HFGDD), obtained from the UCI-ML and Kaggle repository, respectively, and the integrated dataset of these two. The suggested system has been used to demonstrate accuracy, precision, recall, F -measure, latency, arbitration time, jitter, processing time, throughput, energy consumption, bandwidth utilization, network utilization, scalability, and more. In the remote instantaneous diagnosis of diabetic patients, the integration of IoT-fog-cloud is useful. The results of the trials show the value of employing FC principles and their applicability for speedy diabetic patient remote diagnosis. PACS-key is describing text of that key PACS-key describing text of that key.

Modified Transformer Architecture to Explain Black Box Models in Narrative Form

The current XAI techniques present explanations mainly as visuals and structured data. However, these explanations are difficult to interpret for a non-expert user. Here, the use of natural language generation (NLG)-based techniques can help to represent explanations in a human-understandable format. The paper addresses the issue of automatic generation of narratives using a modified transformer approach. Further, due to the unavailability of a relevant annotated dataset for development and testing, the authors also propose a verbalization template approach to generate the same. The input of the transformer is linearized to convert the data-to-text task into text-to-text task. The proposed work is evaluated on a verbalized explained PIMA Indians diabetes dataset and exhibits significant improvement as compared to existing baselines for both manual and automatic evaluation. Also, the narratives provide better comprehensibility to be trusted by human evaluators than the non-NLG counterparts. Lastly, an ablation study is performed in order to understand the contribution of each component.

A Parallel Fractional Lion Algorithm for Data Clustering Based on MapReduce Cluster Framework

This work introduces a parallel clustering algorithm by modifying the existing Fractional Lion Algorithm (FLA). The proposed work replaces the conventional Euclidean distance measure with the Bhattacharya distance measure to newly propose the improved FLA (IMR-FLA). The proposed IMR-FLA is implemented in both the mapper and the reducer in the MapReduce framework to achieve the parallel clustering. The experimentation of the proposed IMR-FLA is done by using six standard databases, namely Pima Indian diabetes dataset, Heart disease dataset, Hepatitis dataset, localization dataset, breast cancer dataset, and skin segmentation dataset, from the UCI repository. The proposed IMR-FLA has the overall improved Jaccard coefficient value of 0.9357, 0.6572, 0.7462, 0.5944, 0.9418, and 0.8680, for each dataset. Similarly, the proposed IMR-FLA algorithm has outclassed other classifiers' performance with the clustering accuracy value of 0.9674, 0.9471, 0.9677, 0.777, 0.9023, and 0.9585, respectively, for the experimental databases.

Diagnostic Analysis of Diabetes Mellitus Using Machine Learning Approach

Diabetes Mellitus (DM) is caused due to the elevated levels of blood sugar i.e., said to be hyperglycemia. The DM is a metabolic chronic disease; therefore, early diagnosis and treatment is necessary to avoid life-threatening risks. According to the World Health Organization (WHO), the diabetes cause high mortality rate with 1.5 million deaths in a year. With the remarkable improvisations in the technology, the disease can be diagnosed earlier. In this paper, we have developed a decision-making support with the machine learning algorithms for DM diagnosis. The Pima Indians Diabetes dataset was chosen to train with Machine Learning algorithms. Our approach begins with Exploratory data analysis, and later the data is sent for data pre-processing and perform the feature Selection techniques. The important features are selected and finally, the data is trained with six various Machine learning (ML) algorithms such as Naïve Bayes, KNN, Random Forest, Logistic Regression, Decision Tree, and eXtreme gradient boosting. The Experimental results of the ML algorithms are calculated by the performance metrics in which that the eXtreme Gradient Boosting has scored highest with 88.2% accuracy than other machine learning algorithms.

Hard Voting Meta Classifier for Disease Diagnosis using Mean Decrease in Impurity for Tree Models

To predict and detect various diseases, machine learning techniques are increasingly being used in the field of medical science. This study puts forward a bagging meta-estimator and feed forward neural network based voting ensemble with mean decrease in impurity feature selection to classify the disease datasets. The work was carried out using the Jupyter notebook data analysis tool, and Python 3 is used as a programming language. In this study, two chronic disease datasets - Indian Liver Patient dataset and the PIMA Indians diabetes dataset are used for building and testing the proposed model. The datasets are split into training and testing data in the ratio of 70:30. The experimental results illustrate that our proposed voting ensemble has an improved performance compared to the individual base learners. We also compared the accuracy of the model before and after the application of feature reduction technique. The results revealed that the accuracy increased with the removal of unimportant features. By using the proposed ensemble model, the average MSE, bias and variance were calculated as 0.311, 0.217 and 0.094 respectively for ILPD dataset. Similarly for PIMA dataset, the average MSE, bias and variance were calculated as 0.233, 0.186 and 0.047 respectively. These statistical parameters record a low score for ensemble classifier as compared to the individual constituent classifiers.

Classification of imbalanced medical data: An empirical study of machine learning approaches

Thousands of patients around the world affecting their health with various factor as age, body mass index, cholesterol levels, albumin levels and several other factor. Prediction of health outcome due to these factors at a proper time can be served as an early warning. Recent growth in machine learning algorithm inspired us to build a predictive model for better healthcare facilities. In our work we have focused on problem of noisy and imbalanced dataset in which majority class is favored over minority one that leads to false prediction. We have experimented with two publicly available medical imbalanced dataset which varies in its size as MIT’s GOSSIS death and PIMA Indians Diabetes Dataset based on binary class. In this model we have investigated 3 oversampling techniques (Synthetic Minority Oversampler, Random Oversampler and Adaptive Synthetic Sampler) along with two undersampling techniques (Random Undersampler and Near Miss) which were paired with 3 data reduction and cleaning methods namely Tomek Links, One Sided Selection and Edited Nearest Neighbors. At last, we found that combination of Adaptive Synthetic Sampler along with One Sided Selection perform better in case of large size dataset while combination of random oversampler along with Tomek Link showed better performance in case of low size data dataset. We have also analyzed that oversampling technique gives quite promising results in comparison to undersampling methods specifically when applied with machine learning classifiers as these classifiers are data hungry algorithms.

A Hybrid Model Focusing on Data Pre-Processing in Diabetes Diagnosis

Diabetes mellitus is a common and serious disease that has been studied by many researchers. Pima Indians Diabetes Dataset is one of the most famous datasets in this field. This study aims to increase the accuracy of machine learning algorithms in diagnosing the disease and to reveal the patterns that enable early diagnosis of the disease by focusing on the pre-processing stages. The proposed hybrid model includes "filling in missing values ​​with KNN", "examining six different normalization methods for normalization" and "removing outliers with K-means" in the pre-processing stage. In the data classification stage, four algorithms C4.5, SVM, Naïve Bayes and KNN were examined and the best hybrid model was found. The performance evaluation of these models is based on accuracy. The results were compared with previous studies and had higher accuracy of 98.3% and 99.1% for (KNN + n5 + K-means + SVM) and (KNN + n4/n3 + K-means + KNN), respectively. Finally, we offer the conclusive notes and some suggestions for further study.

Examination of Diabetes Mellitus for Early Forecast Using Decision Tree Classifier and an Innovative Dependent Feature Vector Based Naive Bayes Classifier

The proposed study aims to evaluate the accuracy and precision in earlier diabetes mellitus detection using Decision Tree and Naive Bayes classification algorithm. Materials and methods: Naive Bayes classifier is applied on a Pima Indian diabetes dataset that consist of 769 records. A machine learning technique for earlier prediction of diabetes disease which compares Decision Tree and Naive Bayes classification algorithms has been proposed and developed. The sample size was measured as 27 per group. The accuracy and precision of the classifiers was evaluated and recorded. Results: The accuracy was maximum in predicting diabetes using Naive Bayesian classifier (76.46%) with minimum mean error when compared with Decision Tree Classifier (70.09%). There is a significant difference of 0.006 between the groups. Hence, Naive Bayes appears to be better than Decision Tree Classifier. Conclusion: The study proves that Naive Bayesian Classifier exhibits better accuracy than Decision Tree Classifier in predicting diabetes.

A Novel Approach for Feature Selection and Classification of Diabetes Mellitus: Machine Learning Methods.

An active research area where the experts from the medical field are trying to envisage the problem with more accuracy is diabetes prediction. Surveys conducted by WHO have shown a remarkable increase in the diabetic patients. Diabetes generally remains in dormant mode and it boosts the other diseases if patients are diagnosed with some other disease such as damage to the kidney vessels, problems in retina of the eye, and cardiac problem; if unidentified, it can create metabolic disorders and too many complications in the body. The main objective of our study is to draw a comparative study of different classifiers and feature selection methods to predict the diabetes with greater accuracy. In this paper, we have studied multilayer perceptron, decision trees, K-nearest neighbour, and random forest classifiers and few feature selection techniques were applied on the classifiers to detect the diabetes at an early stage. Raw data is subjected to preprocessing techniques, thus removing outliers and imputing missing values by mean and then in the end hyperparameters optimization. Experiments were conducted on PIMA Indians diabetes dataset using Weka 3.9 and the accuracy achieved for multilayer perceptron is 77.60%, for decision trees is 76.07%, for K-nearest neighbour is 78.58%, and for random forest is 79.8%, which is by far the best accuracy for random forest classifier.

Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective

Background and ObjectiveDiabetes mellitus is a metabolic disorder characterized by hyperglycemia, which results from the inadequacy of the body to secrete and respond to insulin. If not properly managed or diagnosed on time, diabetes can pose a risk to vital body organs such as the eyes, kidneys, nerves, heart, and blood vessels and so can be life-threatening. The many years of research in computational diagnosis of diabetes have pointed to machine learning to as a viable solution for the prediction of diabetes. However, the accuracy rate to date suggests that there is still much room for improvement. In this paper, we are proposing a machine learning framework for diabetes prediction and diagnosis using the PIMA Indian dataset and the laboratory of the Medical City Hospital (LMCH) diabetes dataset. We hypothesize that adopting feature selection and missing value imputation methods can scale up the performance of classification models in diabetes prediction and diagnosis. MethodsIn this paper, a robust framework for building a diabetes prediction model to aid in the clinical diagnosis of diabetes is proposed. The framework includes the adoption of Spearman correlation and polynomial regression for feature selection and missing value imputation, respectively, from a perspective that strengthens their performances. Further, different supervised machine learning models, the random forest (RF) model, support vector machine (SVM) model, and our designed twice-growth deep neural network (2GDNN) model are proposed for classification. The models are optimized by tuning the hyperparameters of the models using grid search and repeated stratified k-fold cross-validation and evaluated for their ability to scale to the prediction problem. ResultsThrough experiments on the PIMA Indian and LMCH diabetes datasets, precision, sensitivity, F1-score, train-accuracy, and test-accuracy scores of 97.34%, 97.24%, 97.26%, 99.01%, 97.25 and 97.28%, 97.33%, 97.27%, 99.57%, 97.33, are achieved with the proposed 2GDNN model, respectively. ConclusionThe data preprocessing approaches and the classifiers with hyperparameter optimization proposed within the machine learning framework yield a robust machine learning model that outperforms state-of-the-art results in diabetes mellitus prediction and diagnosis. The source code for the models of the proposed machine learning framework has been made publicly available.

Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms.

This paper proposes an e-diagnosis system based on machine learning (ML) algorithms to be implemented on the Internet of Medical Things (IoMT) environment, particularly for diagnosing diabetes mellitus (type 2 diabetes). However, the ML applications tend to be mistrusted because of their inability to show the internal decision-making process, resulting in slow uptake by end-users within certain healthcare sectors. This research delineates the use of three interpretable supervised ML models: Naïve Bayes classifier, random forest classifier, and J48 decision tree models to be trained and tested using the Pima Indians diabetes dataset in R programming language. The performance of each algorithm is analyzed to determine the one with the best accuracy, precision, sensitivity, and specificity. An assessment of the decision process is also made to improve the model. It can be concluded that a Naïve Bayes model works well with a more fine-tuned selection of features for binary classification, while random forest works better with more features.

An Intelligent and Precise Method Used for Detecting Gestational Diabetes in the Early Stages

This paper suggests a Naive Bayes classifier technique for identifying and categorizing gestational diabetes mellitus (GDM), GDM is a kind of diabetes mellitus that affects a small proportion of pregnant women but recovers to normal once the baby is born. The Pima Indians Diabetes Dataset was chosen for a comprehensive analysis of this critical and pervasive health disease because it contains 768 patient characteristics acquired from a machine learning source at the University of California, Irvine. The goal of the study is to apply smart technology to categorize diseases with high accuracy and precision, practically free of conceivable and potential faults, to provide satisfying findings. The approach is based on eight major characteristics that are present in the operations that are required to establish a precise and reliable categorization system. This approach involves training and testing on real data, as well as for deciding whether or not to construct a categorization model. The work was compared to earlier work and had a 96% accuracy rating.

A novel hybrid machine learning framework for the prediction of diabetes with context-customized regularization and prediction procedures

This paper proposes a customized hybrid model of artificial neural network (ANN) and genetic algorithms for an efficient diabetes disease prediction framework. Our customized hybrid model uses an improvised technique of detecting the more visible patterns of relations between the variables. Initially, the input medical dataset is preprocessed using a novel normalization technique that works consistently for all degrees of skewness of data. Then, our proposed decision-making algorithm will correctly identify the degree of importance of each variable in influencing the output, and thus priority will be given to the variables that are deemed most important. This is then followed by the implementation of a regularization method that is custom-made for the prediction of diabetes. Such a customized regularization method is considered asymmetrical because the positive numbers are more favored compared to negative numbers, and this was decided based on the characteristics of the dataset. The proposed technique deals with missing numbers as a separate kind of entity compared to numerical entries and can adapt itself to a given dataset. The proposed customized hybrid model and its accompanying decision-making algorithm were applied to the Pima Indian Diabetes dataset sourced from the UCI Machine Learning Repository with an 80% prediction accuracy.

Diabetes emergency cases identification based on a statistical predictive model

Diabetes is a chronic metabolic disease which is characterized by a permanently high blood sugar level. A distinction is made between two forms: Type 1 diabetes and Type 2 diabetes. It is believed that there are around 415 million people between the ages of 20 and 79 worldwide who have some form of diabetes illness today. In Europe, over 60 million people are diabetic, a diabetes incidence of 10.3% of men and 9.6% of women is estimated. The prevalence of diabetes is increasing among all ages in the European Region, mainly due to increases in overweight and obesity, unhealthy diet, and physical inactivity. A huge people in this population have type 2 diabetes, and the numbers will continue to rise over the next few years. So one can speak of a real widespread disease. The problem is not only the increased blood sugar, but also complications and accompanying diseases such as heart attack, stroke, or diabetic foot. However, as a type 2 diabetic, we can significantly influence the course of the disease and the success of therapy. To do this, it is important that we early detect the person that have (or likely have) a serious problem or an emergent case, and know about it as fast as possible. Early detection and treatment of this disease are very important to help diabetics live a healthy and near normal life. It can also help to avoid several serious complications. In addition, the evolution of wearable and Internet of Things medical devices can help to collect various health data for diagnosis using machine learning algorithms. In this paper, we present an IoT-based system architecture which ensures the collection of patient data in order to predict serious cases of diabetes. To secure data, Blockchain and IPFS are used, and to analyze data, we propose a statistical-based method for predictions. The process is as follows. First, data were collected from IoT devices, and a dataset was constructed and stored using IPFS. Then, the data will be scaled and filtered using noise-invariant data expansion. Next, an adaptive random forest algorithm is made in order to train data on the training dataset, and people with diabetes were classified using the proposed model. Three datasets were used, namely, the Pima Indian diabetes dataset, the Frankfurt Hospital diabetes dataset, and the last is the fusion of these two datasets. Finally, the performance of the method was evaluated and compared with other recent prediction methods. Based on the experiment result, an accuracy of 85.9%, 99.5%, and 99.8% has been achieved based on the three datasets, respectively. Thus, the model can be used to predict and alert physicians or hospitals serious cases that need urgent reactions.

An Efficient Method of Classification the Gestational Diabetes Using ID3 Classifier

Artificial intelligence algorithms have an important and effective role in the medical field, especially in the field of diagnosing diseases. This research focuses on predicting the diagnosis of gestational diabetes by using the Iterative Dichotomiser3 (ID3) classifier algorithm, which is utilized to identify gestational diabetes; it was one of the most significant algorithms employed in this study. Training and testing are two critical phases of the research study. This study employed the Pima Indians Diabetes dataset, which comprised 768 women aged 21 and abovewith the eight reported traits. A feature selection stage, a discretization step, and using the classifier model for producing decision rules are all part of the Pima Indians diabetes data gathering process (Diabetes Dataset). In this study, the decision tree is employed to develop the classifier model, which is based on Diabetes training. The Iterative Dichotomiser3 (ID3) technique may be used to run the decision tree classification process. Diabetes is tested using decision rules, and the classifier implementation confusion matrix was retrieved from the testing portion. The system delivered high-quality results with a 94 percent accuracy rate.

A Hybrid Approach for Enhancing the Classification Accuracy for Diabetes Disease

This paper proposes a new training algorithm for artificial neural networks based on an enhanced version of the grey wolf optimizer (GWO) algorithm. The proposed model is used for classifying the patients of diabetes disease. The results showed that the proposed training algorithm enhanced the performance of ANNs with a better classification accuracy as compared to the other state of art training algorithms for the classification of diabetes on publicly available “Pima Indian Diabetes (PID) dataset”. Several experiments have been executed on this dataset with variation in size of the population, techniques to handle missing data, and their impact on classification accuracy has been discussed. Finally, the results are compared with other nature-inspired algorithms trained ANN. EGWO attained better results in terms of classification accuracy than the other algorithms. The convergence curve proved that EGWO had balanced the local and global search abilities because it was faster to reach better positions than the original GWO.