A Diabetic Disease Prediction Model Based on Classification Algorithms

Abstract

Diabetes is one of the chronic diseases in the world, 246 million people are inflicted by this disease and according to a World Health Organisation (WHO) report, this figure will increase to 380 million sufferers by 2025. Many other debilitating and critical health issues may further develop if this disease is not diagnosed or remain unidentified. Machine Learning (ML) techniques are now being used in various fields like education, healthcare, business, recommendation system, etc. Healthcare data is complex and high in dimensionality and contains irrelevant information - due to this, the prediction accuracy is low. The Pima Indians Diabetes Dataset was used in this research, it consisted of 768 records. Firstly, the missing values are replaced by the median followed by Linear Discriminant Analysis. Using the Python programming language, feature selection techniques is applied in combination with five classification algorithms: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Logistic Regression, Random Forest and Decision Tree. The aim of this paper is to compare the different classification algorithms in order to predict diabetes in patients more accurately. K-fold cross-validation is applied, considering k to be 2, 4, 5 and 10. The performance parameters taken are the: accuracy, precision, recall, F Score and area under the curve. Our study found that the MLP classifier gave the highest accuracy of 78.7% with a recall of 61.26%, precision of 72.45% and F1 Score of 65.97% for k = 4.

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

“…ey used USbased National Health and Nutrition Survey data of diabetic and nondiabetic individuals and achieved promising results with the proposed technique. Ahuja et al [20] performed a comparative analysis of various machine learning approaches, i.e., NB, DT, and MLP, on the PIMA dataset for diabetic classification. ey found MLP superior as compared to other classifiers.…”

“…First, to classify diabetes, we utilized logistic regression, random forest, and MLP. Notably, we fine-tuned MLP for classification due to its promising performance in healthcare, specifically in diabetes prediction [20,21,35,36]. e proposed MLP architecture and algorithm are shown in Figure 2 and Algorithm 1, respectively.…”

Automating the Assessment of Networking and Security in Higher Education

“…In this case a solution template could be refined by the process of supervised machine learning. This method is used widely in various fields, including education [17]. However this would depend on the output from a large number of students for a particular exercise across a wide range of outcomes [18].…”

“…The simplicity of the current system is that it compares a system under test, in this case the output of a time constrained assignment, with a known solution. The current parser mechanism is interchangeable and more sophisticated mechanisms for assessing work could be investigated, which might eventually lead to an expert system or other machine learning approaches specifically applied to networking and security education [17], [18], [19].…”

Effectiveness of Classification Methods on the Diabetes System

“…Using classification methods in the medical field is considered to be very widely used in today's world trying to approach better detection or even vaccines for certain diseases under certain constraints. Diabetes is considered to be a chronic disease in the world and it's about the Case Study level of sugar in the blood, where it becomes too high [1,2]. Diabetes Mellitus (DM) is an illness that causes the human body to lower the production of the insulin hormone, which in return makes the level of glucose raises, and the metabolism and especially of carbohydrates abnormal.…”