Diabetes is a dreadful disease identified by escalated levels of glucose in the blood Machine learning algorithms help in identification and prediction of diabetes at an early stage The main objective of this study is to predict diabetes mellitus with better accuracy using an ensemble of machine learning algorithms The Pima Indians Diabetes dataset has been considered for experimentation, which gathers details of patients with and without having diabetes The proposed ensemble soft voting classifier gives binary classification and uses the ensemble of three machine learning algorithms viz random forest, logistic regression, and Naive Bayes for the classification Empirical evaluation of the proposed methodology has been conducted with state-of-the-art methodologies and base classifiers such as AdaBoost, Logistic Regression,Support Vector machine, Random forest, Naive Bayes, Bagging, GradientBoost, XGBoost, CatBoost by taking accuracy, precision, recall, F1-score as the evaluation criteria The proposed ensemble approach gives the highest accuracy, precision, recall, and F1_score value with 7904%, 7348%, 7145% and 806% respectively on the PIMA diabetes dataset Further, the efficiency of the proposed methodology has also been compared and analysed with breast cancer dataset The proposed ensemble soft voting classifier has given 9702% accuracy on the breast cancer dataset

An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier

Diabetes is a disease caused due of the expanded level of sugar fixation in the blood. Various computerized information systems were outlined utilizing diverse classifiers for anticipating and diagnosing diabetes. Selecting legitimate classifiers clearly expands the exactness and proficiency of the system. Here a decision support system is proposed that uses AdaBoost algorithm with Decision Stump as base classifier for classification. Additionally Support Vector Machine, Naive Bayes and Decision Tree are also implemented as base classifiers for AdaBoost algorithm for accuracy verification. The accuracy obtained for AdaBoost algorithm with decision stump as base classifier is 80.72% which is greater compared to that of Support Vector Machine, Naive Bayes and Decision Tree.

Prediction and diagnosis of diabetes mellitus — A machine learning approach

The diabetes mellitus disease (DMD) commonly referred as diabetes is a significant public health problem. Predicting the disease at the early stage can save the valuable human resource. Voluminous datasets are available in various medical data repositories in the form of clinical patient records and pathological test reports which can be used for real-world applications to disclose the hidden knowledge. Various data mining (DM) methods can be applied to these datasets, stored in data warehouses for predicting DMD. The aim of this research is to predict diabetes based on some of the DM techniques like classification and clustering. Out of which, classification is one of the most suitable methods for predicting diabetes. In this study, J48 and Naive Bayesian techniques are used for the early detection of diabetes. This research will help to propose a quicker and more efficient technique for diagnosis of disease, leading to timely and proper treatment of patients. We have also proposed a model and elaborated it step-by-step, in order to make medical practitioner to explore and to understand the discovered rules better. The study also shows the algorithm generated on the dataset collected from college medical hospital as well as from online repository. In the end, an article also outlines how an intelligent diagnostic system works. A clinical trial of this proposed method involves local patients, which is still continuing and requires longer research and experimentation.

Classification of Diabetes Mellitus Disease (DMD): A Data Mining (DM) Approach

Type 2 diabetes has a quite high incidence all over the world. For the prevention and treatment of Type 2 diabetes, early detection is demanded. Nowadays, data mining techniques are gaining increasing importance in medical diagnosis field by their classification capability. In this paper, a hybrid prediction model is proposed to help the diagnosis of Type 2 diabetes. In the proposed model, K-means is used for data reduction with J48 decision tree as a classifier for classification. In order to get the experimental result, we used the Pima Indians Diabetes Dataset from UCI Machine Learning Repository. The result shows that the proposed model has reached better accuracy compared to other previous studies that mentioned in the literature. On the basis of the result, it can be proven that the proposed model would be helpful in Type 2 diabetes diagnosis.

A hybrid prediction model for type 2 diabetes using K-means and decision tree

Diabetes has become one of the major health concerns for the modern day population. This can be attributed to a number of factors such as unhealthy lifestyle, meager diet, genetics, obesity, etc. The rapid growth in the number of diabetic patients urges the requirement for a state-of-the-art healthcare against such diseases. Early prediction of such diseases can be very useful for mitigating the risks associated with such diseases. In this context, this research proposes an indigenous efficient diagnostic tool for the detection of diabetes. The proposed methodology comprises two phases: Phase-I deals with collection of Pima Indian Diabetes Dataset from the UCI machine learning repository databases and Localized Diabetes Dataset from Bombay Medical Hall, Upper Bazar Ranchi, Jharkhand, India. In Phase-II, the acquired datasets are processed and analyzed using two different approaches. The first approach entails classification through Logistic Regression, K-Nearest Neighbor, ID3 DT, C4.5 DT, and Naive Bayes. The second approach employs PCA and PSO algorithms for feature reduction prior to the classification of the dataset using the methods used in the first approach. A comparative analysis is performed between the various approaches used in this manuscript. Results obtained clearly depict the efficiency of the proposed approach over the traditional classification approach in terms of less computation time and increased accuracy. The proposed approach has the potential to be applied for effective and early diagnosis of other medical diseases as well.

Performance evaluation of classification methods with PCA and PSO for diabetes

mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various available traditional methods for diagnosing diabetes are based on physical and chemical tests. These methods can have errors due to different uncertainties. A number of Data mining algorithms were designed to overcome these uncertainties. Among these algorithms, amalgam KNN and ANFIS provides higher classification accuracy than the existing approaches. The main data mining algorithms discussed in this paper are EM algorithm, KNN algorithm, K-means algorithm, amalgam KNN algorithm and ANFIS algorithm. EM algorithm is the expectation-maximization algorithm used for sampling, to determine and maximize the expectation in successive iteration cycles. KNN algorithm is used for classifying the objects and used to predict the labels based on some closest training examples in the feature space. K means algorithm follows partitioning methods based on some input parameters on the datasets of n objects. Amalgam combines both the features of KNN and K means with some additional processing. ANFIS is the Adaptive Neuro Fuzzy Inference System which combines the features of adaptive neural network and Fuzzy Inference System. The data set chosen for classification and experimental simulation is based on Pima Indian Diabetic Set from University of California, Irvine (UCI) Repository of Machine Learning databases. Keywordsmining, Diabetes, EM algorithm, KNN algorithm, K- means algorithm, amalgam KNN algorithm, ANFIS algorithm

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Veena Vijayan

Papers

Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus