Pima Indian Diabetes Research Articles

Diabetes prediction using feature engineering and machine learning algorithms with security

The prevalence of diabetes has been steadily increasing, necessitating accurate prediction models to assist in early diagnosis and proactive management. In this paper, a hybrid machine learning-based diabetes prediction model has been proposed. To evaluate the model, the dataset was subsequently divided into training and testing subsets. We used the Random Forest Classifier, Light Gradient Boosting Mechanism Classifier, Gradient Boosting Classifier, Logistic Regression, K-Nearest Neighbours (KNN) Classifier, Naive Bayes Gaussian, Decision Tree Classifier, XGBoost Classifier, and Support Vector Classifier as nine different classifiers. Several metrics were used to evaluate the models, including testing accuracy, recall score, F1 score, and precision score. We have evaluated our model on the “Pima Indian Diabetes Database”[1], which served as the main dataset, for diabetes prediction. The proposed model serves as a practical framework for researchers and practitioners interested in leveraging machine learning techniques for diabetes prediction.

A Weighted Majority Voting Ensemble Model for Disease Prediction Boosted by PSO: The Case of Type 2 Diabetes

Early detection of diabetes is critical to reducing the number of cases, which continues to rise year after year. Many approaches to diagnosis have been used, but they still have flaws in making clinical decisions that are more effective and efficient. The use of intelligent systems is very effective in assisting in data analysis and clinical decision support. The purpose of this article is to develop a model to predict diabetes mellitus using the Pima Indian Diabetes Dataset (PIDD). The ensemble method has shown to be quite effective at increasing accuracy, but it has the issue of determining the optimal weight. As a result, to improve prediction accuracy, this study employs PSO optimization in the selection of ensemble majority voting weights. The test results show that predictions for ensemble majoritarian voting using PSO perform well, with an accuracy rate of 0.9322, precision of 0.9412, recall of 0.8421, and F1-score of 0.8889. PSO accuracy is improved by 4% and 7%, respectively. This demonstrates that applying PSO to the ensemble model can improve accuracy.

Binary Classification of Diabetes in Pima Indian Dataset: A Deep Learning Perspective

Abstract: In this study, we develop into the application of deep learning methodologies for diabetes prediction utilizing the Pima Indian dataset. Employing Keras with Theano as the backend, we establish a binary classification model to effectively forecast the presence or absence of diabetes in individuals. Our research aims to enhance the precision and reliability of diabetes diagnosis, ultimately contributing to improved healthcare decision-making. Our investigation leverages Keras, a high-level neural networks API, in conjunction with Theano, to conduct binary classification on the Pima Indian diabetes dataset. Our study provides valuable insights into the field of medical data analysis, showcasing the effectiveness of deep learning techniques in advancing diagnostic tools for proactive healthcare management. Diabetes mellitus, a prevalent chronic disease globally, necessitates the development of a system for early type 2 diabetes mellitus (T2DM) diagnosis. Multiple machine learning and data mining techniques, including ANN, SVM, KNN, decision trees, and Extreme Learning Machines, have emerged and been employed as aids in diabetes detection. Consequently, we introduce Deep Learning, a subfield of machine learning, which can effectively handle smaller datasets through efficient data processing techniques. This paper presents an in-depth review of Diabetic Retinopathy, covering its features, causes, various ML models, DL models, challenges, comparisons, and future directions for early DR detection. Diabetes mellitus is a global health concern with a rapidly increasing prevalence. In this context, machine learning technologies prove invaluable for early disease identification and diagnosis. The focus of this study is to identify the most effective ML algorithm for diabetes prediction.

Prediction Using SVM and DT

Diabetes is recognized as a severe and persistent illness that leads to an elevation in blood sugar levels. Untreated and undetected diabetes can give rise to numerous complications. The laborious process of identification typically involves a patient visiting a diagnostic centre and consulting with a doctor. However, the advancement of machine learning approaches has addressed this critical issue. The objective of this study is to develop a model capable of predicting the likelihood of diabetes in patients with the utmost accuracy. To achieve this goal, three machine learning classification algorithms—Decision Tree, SVM, and Naive Bayes—are employed in this experiment to identify diabetes at an early stage. The experiments are conducted on the Pima Indians Diabetes Database (PIDD), sourced from the UCI machine learning repository. The performance of each algorithm is assessed using various metrics such as Precision, Accuracy, F-Measure, and Recall. Accuracy is gauged based on both correctly and incorrectly classified instances. The results indicate that Naive Bayes outperforms the other algorithms, achieving the highest accuracy of 76.30%. These findings are corroborated through a meticulous examination of Receiver Operating Characteristic (ROC) curves.

Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

An essential aspect of healthcare is receiving an appropriate and opportune disease diagnosis. In recent years, there has been enormous progress in combining artificial intelligence to help professionals perform these tasks. The design of interval Type-3 fuzzy inference systems (IT3FIS) for medical classification is proposed in this work. This work proposed a genetic algorithm (GA) for the IT3FIS design where the fuzzy inputs correspond to attributes relational to a particular disease. This optimization allows us to find some main fuzzy inference systems (FIS) parameters, such as membership function (MF) parameters and the fuzzy if-then rules. As a comparison against the proposed method, the results achieved in this work are compared with Type-1 fuzzy inference systems (T1FIS), Interval Type-2 fuzzy inference systems (IT2FIS), and General Type-2 fuzzy inference systems (GT2FIS) using medical datasets such as Haberman’s Survival, Cryotherapy, Immunotherapy, PIMA Indian Diabetes, Indian Liver, and Breast Cancer Coimbra dataset, which achieved 75.30, 87.13, 82.04, 77.76, 71.86, and 71.06, respectively. Also, cross-validation tests were performed. Instances established as design sets are used to design the fuzzy inference systems, the optimization technique seeks to reduce the classification error using this set, and finally, the testing set allows the validation of the real performance of the FIS.

An efficient prediction system for diabetes disease based on machine learning algorithms

Diabetes is a persistent medical condition that arises when the pancreas loses its ability to produce insulin or when the body is unable to utilize the insulin it generates effectively. In today's world, diabetes stands as one of the most prevalent and, unfortunately, one of the deadliest diseases due to certain complications. Timely detection of diabetes plays a crucial role in facilitating its treatment and preventing the disease from advancing further. In this study, we have developed a diabetes prediction model by leveraging a variety of machine learning classification algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression, to determine which algorithm yields the most accurate predictive outcomes. we employed the famous PIMA Indians Diabetes dataset, comprising 768 instances with nine distinct feature attributes. The primary objective of this dataset is to ascertain whether a patient has diabetes based on specific diagnostic metrics included in the collection. In the process of preparing the data for analysis, we implemented a series of preprocessing steps. The evaluation of performance metrics in this study encompassed accuracy, precision, recall, and the F1 score. The results from our experiments indicate that the K-nearest neighbors’ algorithm (KNN) surpasses other algorithms in effectively differentiating between individuals with diabetes and those without in the PIMA dataset.

A novel evolutionary ensemble prediction model using harmony search and stacking for diabetes diagnosis

Diabetes is a dreaded disease that can be identified by elevated blood glucose levels in the blood, and undiagnosed diabetes can cause a host of related complications, such as retinopathy and nephropathy. In terms of type, the main categories are type 1 diabetes (T1DM), type 2 diabetes (T2DM) and gestational diabetes mellitus (GDM). Machine learning models and metaheuristic optimization algorithms can play an important role in the early detection, diagnosis and treatment of this disease. To this end, we propose AHDHS-Stacking, an ensemble learning framework for diabetes mellitus classification and diagnosis that is based on the harmony search (HS) algorithm and stacking and includes two stages of feature selection and optimization of base-learner combinations. To improve the model’s overall performance, the average performance of all base learners is used as the feature selection target, and an adaptive hyperparameter strategy is used to accelerate the iterative process. HS is then used to optimize to find the best combination of base learners, which improves model performance while reducing complexity. Following that, we conducted experiments on the Pima Indians Diabetes (PID) dataset and the Chinese and Western Medicine Diabetes (CWMD) dataset, achieving accuracy of 93.09%, precision of 93.22%, recall of 91.60% , F-measure of 92.25%, and MCC of 84.79% on PID dataset, which is better than all benchmark models and validated the model’s validity. CWMD dataset experimental results showed that AHDHS-Stacking screened for key features such as age, gender, urinary glucose, fasting glucose, BMI and cholesterol, and can be used as a practical and accurate method for early diabetes prediction.

Optimized stacking ensemble for early-stage diabetes mellitus prediction

<div align="center"><span lang="EN-US">This paper presents an optimized stacking-based hybrid machine learning approach for predicting early-stage diabetes mellitus (DM) using the PIMA Indian diabetes (PID) dataset and early-stage diabetes risk prediction (ESDRP) dataset. The methodology involves handling missing values through mean imputation, balancing the dataset using the synthetic minority over-sampling technique (SMOTE), normalizing features, and employing a stratified train-test split. Logistic regression (LR), naïve Bayes (NB), AdaBoost with support vector machines (AdaBoost+SVM), artificial neural network (ANN), and k-nearest neighbors (k-NN) are used as base learners (level 0), while random forest (RF) meta-classifier serves as the level 1 model to combine their predictions. The proposed model achieves impressive accuracy rates of 99.7222% for the ESDRP dataset and 94.2085% for the PID dataset, surpassing existing literature by absolute differences ranging from 10.2085% to 16.7222%. The stacking-based hybrid model offers advantages for early-stage DM prediction by leveraging multiple base learners and a meta-classifier. SMOTE addresses class imbalance, while feature normalization ensures fair treatment of features during training. The findings suggest that the proposed approach holds promise for early-stage DM prediction, enabling timely interventions and preventive measures.</span></div>

A Novel Diabetes Prediction Model in Big Data Healthcare Systems Using DA-KNN Technique

In the past decades, there is a wide increase in the number of people affected by diabetes, a chronic illness. Early prediction of diabetes is still a challenging problem as it requires clear and sound datasets for a precise prediction. In this era of ubiquitous information technology, big data helps to collect a large amount of information regarding healthcare systems. Due to explosion in the generation of digital data, selecting appropriate data for analysis still remains a complex task. Moreover, missing values and insignificantly labeled data restrict the prediction accuracy. In this context, with the aim of improving the quality of the dataset, missing values are effectively handled by three major phases such as (1) pre-processing, (2) feature extraction, and (3) classification. Pre-processing involves outlier rejection and filling missing values. Feature extraction is done by a principal component analysis (PCA) and finally, the precise prediction of diabetes is accomplished by implementing an effective distance adaptive-KNN (DA-KNN) classifier. The experiments were conducted using Pima Indian Diabetes (PID) dataset and the performance of the proposed model was compared with the state-of-the-art models. The analysis after implementation shows that the proposed model outperforms the conventional models such as NB, SVM, KNN, and RF in terms of accuracy and ROC.

Classification and Prediction Evaluation of a Fuzzy Rule-Based Model for Diabetes Diagnosis, Level of Care and Pima Indians Diabetes Datasets

— Classification and prediction of diseases is crucial in decision-making for the healthcare sectors, especially diabetes, a chronic disease. The availability and accessibility of diabetes datasets assists medical practitioners in the diagnosis process as well as researchers in various fields and these datasets are valuable sources. However, diabetes datasets are exposed to vagueness and uncertainty issues. This research work improved an interrelated decision-making model for diabetes by proposing a fuzzy rule-based model to handle the vagueness and uncertainty issues. The research methodology starts with pre-processing of the simulated diabetes diagnosis and level of care datasets that were validated by medical experts, as well as the Pima Indians Diabetes Dataset (PIDD). This is followed by the design of the fuzzy model and the construction of the fuzzy rules. Next, the testing of the fuzzy model using six supervised machine learning algorithms namely J48, Logistic, Naive Bayes Updateable, Random Tree, Bayes Net and AdaBoostM1. Lastly, the evaluation of the fuzzy model in terms of the accuracy, precision, recall, F1-Score and confusion matrix. Experimental results show 100% accuracy for the diabetes diagnosis fuzzy model, for all the five machine learning algorithms mentioned except AdaBoostM1 with 79.8165% accuracy. In addition, for the level of care fuzzy model, the highest accuracy produced is 97.1098% for J48 algorithm and the lowest accuracy is 93.1049% for Naive Bayes Updateable and Bayes Net algorithms. Furthermore, for the PIDD fuzzy model, the highest accuracy obtained is 74.8698% for J48 and AdaBoostM1 algorithms and the lowest accuracy is 70.1823% for Random Tree algorithm. Overall, the proposed fuzzy model produced a good accuracy and working as expected associated to the previous interrelated decision-making model.

Heart disease severity level identification system on Hyperledger consortium network.

Electronic Health Records (EHRs) play a vital role in the healthcare domain for the patient survival system. They can include detailed information such as medical histories, medications, allergies, immunizations, vital signs, and more. It can help to reduce medical errors, improve patient safety, and increase efficiency in healthcare delivery. EHR approaches are proven to be an efficient and successful way of sharing patients' personal health information. These kinds of highly sensitive information are vulnerable to privacy and security associated threats. As a result, new solutions must develop to meet the privacy and security concerns in health information systems. Blockchain technology has the potential to revolutionize the way electronic health records (EHRs) are stored, accessed, and utilized by healthcare providers. By utilizing a distributed ledger, blockchain technology can help ensure that data is immutable and secure from tampering. In this article, a Hyperledger consortium network has been developed for sharing health records with enhanced privacy and security. The attribute based access control (ABAC) mechanism is used for controlling access to electronic health records. The use of ABAC on the network provides EHRs with an extra layer of security and control, ensuring that only authorized users have access to sensitive data. By using attributes such as user identity, role, and health condition, it is possible to precisely control access to records on blockchain. Besides, a Gaussian naïve Bayes algorithm has been integrated with this consortium network for prediction of cardiovascular disease. The prediction of cardiovascular is difficult due to its correlated risk factors. This system is beneficial for both patients and physicians as it allows physicians to quickly identify high-risk patients and easily provide them with patient severity level using feature weight prediction algorithms. Dynamic emergency access control privileges are used for the emergency team and will be withdrawn once the emergency has been resolved, depending on the severity score. The system is implemented with the following medical datasets: the heart disease dataset, the Pima Indian diabetes dataset, the stroke prediction dataset, and the body fat prediction dataset. The above datasets are obtained from the Kaggle repository. This system evaluates system performance by simulating various operations using the Hyperledger Caliper benchmarking tool. The performance metrics such as latency, transaction rate, resource utilization, etc. are measured and compared with the benchmark.

Comparison of Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) Algorithms for Diabetes Classification

Purpose: This study aims to compare algorithms to determine the accuracy of the algorithm and determine the speed of the algorithm used for diabetes classification.
 Methods: There are two algorithms used in this study, namely Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN). The data used is the Pima Indians Diabetes Database. The data contains 768 data with 8 attributes and 1 target class, namely 0 for no diabetes and 1 for diabetes. The dataset has been divided into 80% training data and 20% testing data.
 Result: Accuracy is obtained after implementing k-fold cross validation with a value of k = 4. The accuracy results show that the k-Nearest Neighbor algorithm is superior and has better quickness compared to the Probabilistic Neural Network. The k-Nearest Neighbor algorithm obtains an accuracy of 74.6% for all features and 78.1% for four features
 Novelty: The novelty of this paper is optimizing and improving accuracy which is implemented with by focusing on data preprocessing, feature selection and k-fold cross validation in the classification algorithm

Role of transfer functions in PSO to select diagnostic attributes for chronic disease prediction: An experimental study

Particle Swarm Optimization (PSO) is a classic and popularly used meta-heuristic algorithm in many real-life optimization problems due to its less computational complexity and simplicity. The binary version of PSO, known as BPSO, is used to solve binary optimization problems, such as feature selection. Like other meta-heuristic optimization techniques designed on the continuous search space, PSO uses the transfer functions (TFs) to map the candidate solutions to the discrete search space in BPSO, and these TFs play a vital role to get the desired results. Over the years, many forms of TFs have been introduced in the literature, most of which fall under one of the five families - Linear, S-shaped, V-shaped, U-shaped, and Time-varying Mirrored S-shaped TFs. The goal of this study is to determine an appropriate setup constituting a TF and a classifier for feature selection from different types of clinical data. In this study, the impacts of the five TF families have been investigated, considering one from each family for the selection of attributes/features, while predicting disease using diagnosis or medical reports. The classification tasks are carried out using four standard classifiers: Support Vector Machine, Decision Tree, K-Nearest Neighbors, and Gaussian Naive Bayes. For experimental purposes, we have used four publicly available datasets namely, the UCI Heart Disease dataset, Wisconsin Breast Cancer dataset, UCI Chronic Kidney Disease dataset, and PIMA Indians Diabetes dataset. After an exhaustive set of experiments, we have obtained 96.72%, 99.82%, 100.00%, and 84.41% disease prediction scores in the best case for Heart disease, Breast Cancer, Chronic Kidney disease, and Diabetes, respectively. The obtained results are comparable to several state-of-the-art methods considered here for comparison. The present study helps in selecting a suitable BPSO setup (i.e., a TF and a classifier) to select important diagnostic attributes useful to design a computer-aided decision support system for the said diseases.

Disease Prediction Using a Modified Multi-Layer Perceptron Algorithm in Diabetes

This paper presents an adaptation of the Multi-Layer Perceptron (MLP) algorithm for use in predicting diabetes risk. The aim is to enhance the accuracy and generalizability of the model by incorporating preprocessing techniques, dimensionality reduction using Principal Component Analysis (PCA), and improvements in optimization and regularization. Several factors, including glucose level, pregnancy, blood pressure, and body mass index, are taken into account when analyzing the PIMA Indian Diabetes dataset. Modern optimization methods, dropout regularization, and an adaptive learning rate are incorporated into the modified MLP model to fine-tune the model's weights and boost its predictive abilities. The effectiveness of the modified MLP algorithm is evaluated by comparing its performance with baseline machine learning methods and the original MLP algorithm in terms of accuracy, sensitivity, and specificity. The results of this study can improve the quality of healthcare provided to people at risk for developing diabetes and thus contribute to the development of better prediction models for the disease.

Performance comparison machine learning algorithms in diabetes disease prediction

Machine learning has been widely used in the field of medicine with the developing technology in recent years. Machine learning is a field that is also used in the diagnosis of diabetes and helps experts make decisions. Diabetes is a lifelong disease that is common worldwide and in our country. The main purpose of this study is to diagnose diabetes early using different machine learning classification algorithms. Another purpose of the study is to compare the success of the machine learning models used. Early diagnosis of diabetes allows to lead a healthy and normal life. In this context, it has been tried to diagnose diabetes early by using the machine learning techniques Decision Tree, Random Forests, K-Nearest Neighbor and Support Vector Machines classifiers on the Pima Indians Diabetes dataset. The dataset includes 9 features and 768 samples. Success evaluation of classifiers was made using Accuracy, Precision, Recall, F1-Score and AUC metrics. Random Forests gave the best results with 80 percent accuracy. This paper is to examine the association of different machine learning techniques usage, diabetes data diagnostic capabilities, diagnosis of diabetes in women diabetes patients and comparison of performances for machine learning techniques. Implications for theory and practice have been discussed. In this study, comparisons were made using different algorithms from the classification algorithms used in the literature and contributed to the literature in this field.

Type-2 Diabetes prediction with cuttlefish search optimization and hybrid model

In India, there are over 30 million individuals with diabetes and many more who are at risk. Diabetes and related health issues must be detected and treated early to be prevented. This study looks at a person's lifestyle and family history to determine their risk of having diabetes. The suggested hybrid machine learning approach, which combines the Support Vector Machine (SVM) classifier and Naive Bayes (NB) classifier, was used to forecast the probability of Type 2 diabetes. We incorporate Cuttlefish Search Optimization (CFSO) to increase prediction accuracy. After the model has been successfully trained, individuals may evaluate their own risk of developing diabetes. 952 times the information required for the question using an online and offline survey along with 18 questions about your habits, health, and family history. Preprocessing was done using min-max normalization, and feature extraction was done using linear discriminant analysis (LDA). The recommended approach was also examined using the Pima Indian Diabetes database. The performance of the provided approach is proved to be the most accurate for both datasets. The term "Type 2 Diabetes prediction with Cuttlefish Search optimization and a hybrid model" refers to the employment of a particular optimization technique called Cuttlefish Search in conjunction with a hybrid model to forecast a person's chance of getting Type 2 Diabetes.

Early prediction of diabetes diagnosis using hybrid classification techniques

<span lang="EN-US">Diabetes can be mentioned as one of the most lethal and constant sicknesses that may cause an arise in the glucose levels. Design and development of performance efficient diagnosis tool is important and plays a vigorous role in initial prediction of disease and help medical experts to start with suitable treatment or medication. The insulin produced by pancreases in the subject’s body will be affected leading to several dysfunctionalities to various body organs such as kidney, heart eyes and nervous system with their normal functionalities. Hence, preliminary stage detection with proper care and medication could reduce the risk of these problems. In the area of medicine to discover patient’s data as well as to attain a predictive model or a set of rules, classification techniques have been continuously used. This study helped diagnose diabetes by selecting three important artificial intelligence techniques namely the optimal decision tree algorithm model, Type-2 fuzzy expert system and adaptive neuro fuzzy inference system which is modified. In the present research work, a hybrid model is proposed in order to improve the classification prediction and accuracy. The Pima Indian diabetes dataset from machine learning repository dataset was used to carry out validation and predication of the model accuracy.</span>

Machine Learning Approach for Diabetes Detection Using Fine-Tuned XGBoost Algorithm

Diabetes is a chronic condition characterized by elevated blood glucose levels which leads to organ dysfunction and an increased risk of premature death. The global prevalence of diabetes has been rising, necessitating an accurate and timely diagnosis to achieve the most effective management. Recent advancements in the field of machine learning have opened new possibilities for improving diabetes detection and management. In this study, we propose a fine-tuned XGBoost model for diabetes detection. We use the Pima Indian Diabetes dataset and employ a random search for hyperparameter tuning. The fine-tuned XGBoost model is compared with six other popular machine learning models and achieves the highest performance in accuracy, precision, sensitivity, and F1-score. This study demonstrates the potential of the fine-tuned XGBoost model as a robust and efficient tool for diabetes detection. The insights of this study advance medical diagnostics for efficient and personalized management of diabetes.

Diabetes Mellitus Early Detection Simulation using The K-Nearest Neighbors Algorithm with Cloud-Based Runtime (COLAB)

Diabetes Mellitus is a genetically and clinically heterogeneous metabolic disorder with manifestations of loss of carbohydrate tolerance characterized by high blood glucose levels as a result of insulin insufficiency. Public knowledge of diabetes mellitus 39.30% is influenced by public health education and information about diabetes mellitus that the public has ever received. Early detection of diabetes mellitus can prevent the development of chronic complications and allow timely and rapid treatment. The aim of this study is to simulate the early detection of diabetes mellitus with the K-Nearest Neighbors (K-NN) algorithm using Cloud-Base Runtime (COLAB). The highest accuracy is 76% in K=3, the highest precision is 68% in K=3 and the highest recall is 60% in K=3. The researchers used K-NN as a method to classify data from the Pima Indians Diabetes Database and obtained a fairly good accuracy value of 76% with a value of k = 3.

PERBANDINGAN BERBAGAI MODEL MACHINE LEARNING UNTUK MENDETEKSI DIABETES

Diabetes mellitus, often known as diabetes, is a significant metabolic illness that has a negative impact on living organisms. It causes high blood sugar levels by either creating inadequate insulin or using it inefficiently. Diabetes that is not effectively treated raises the risk of heart attacks, retinopathy, vision loss, skin disorders, and other ailments. Early detection is critical for guiding essential actions. In this setting, machine learning (ML) has emerged as a potent tool. We used Python data manipulation tools to develop ML techniques for discovering patterns and risk factors in the Pima Indian diabetes dataset in our study. We correctly identified patients as diabetes or non-diabetic using K-Nearest Neighbors (KNN), AdaBoost, Logistic Regression (LR), Light Gradient Boosting, Random Forest (RF), dan Support Vector Machine (SVM). Notably, we used the Synthetic Minority Over-sampling Technique (SMOTE) to solve class imbalance, which enhanced model performance. By efficiently utilizing ML and SMOTE in diabetes categorization, our work greatly adds to the scientific area. We suggest studying cutting-edge technology and undertaking external validation and clinical studies to assure trustworthy and generalized models for diabetic patient care in the future. With diabetes's increasing prevalence, such improvements have enormous promise for improving early identification and management, eventually leading to better health outcomes.