Supervised Learning Method and Neural Network Algorithm for the Analysis of Diabetic Mellius and its Comparitive Analysis
01 Jan 2021-pp 463-475
TL;DR: In this system, the predictive methodologies, various classification techniques are discussed and the results are analyzed, it is implied that men between ages 15-49 among 1 billion people have reported with diabetes mellitus.
Abstract: Diabetes is the key critical issue needs to be concerned for various problems in our body. Increase in glucose and fructose content in our body results in diabetes mellitus. When a body generates higher insulin level than the required, it results in increased urination and excessive thirstiness which in turn results in kidney failure and other cardio-related issues. Many research agencies invested their funds on defining the predictive methodology and finding the root cause of those results in mellitus. Mellitus results in the highest mortality rate compared to any other disease reported by the health organizations across the globe. In this, the predictive methodologies, various classification techniques are discussed, and the results are analyzed. The classification methodology could be on medications, food habits, personal behaviors, age factors and so on. The datasets are processed and analyzed with the neural network algorithms, and the results are compared with one another. The datasets are taken from the National Family Health Survey results published during the period of 2016–2017. The result implies that men between ages 15–49 among 1 billion people have reported with diabetes mellitus. Diagnose and forecast on this disease are done by recognizing the pattern formation and grouping the similar structures. Various algorithmic techniques like M-layer perceptron, nearest neighbor, vector machines, data regressions, binary regression and their accuracy of forecast, speed and sensitivity are calculated, analyzed and compared to define the accurate prediction methodology over a short span of time. The forecast methodologies are focussed to provide solutions to avoid the intensive care system provided proper medications with a long duration when it is been predicted to be a risk factor. A statistical method of analyzing is performed for the comparative analysis. The learning and training methodologies are discussed in this system. Accuracy, specificity, sensitivity are the key parameters to define the best forecast methodology. Classification on association, regression techniques and neural algorithmic techniques is analyzed and compared to refine the best predictive forecast methodology by processing 30 samples across the states of India with focus on determining the type of mellitus along with the accuracy on definition. The forecast data utilized to define the type of mellitus and the prediction on critical measures over a period of time.
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TL;DR: This work uses neural networks to predict the onset of diabetes mellitus in Pima Indian women and attempts to provide a basis upon which neural networks can be used for variable selection in statistical modeling.
Abstract: Classification is an important decision making tool, especially in the medical sciences. Unfortunately, while several classification procedures exist, many of the current methods fail to provide adequate results. In recent years, artificial neural networks have been suggested as an alternative tool for classification. Here, we use neural networks to predict the onset of diabetes mellitus in Pima Indian women. The modeling capabilities of neural networks are compared to traditional methods like logistic regression and to a specific method called ADAP, which has been used to predict diabetes. The results indicate that neural networks are indeed a viable approach to classification. Furthermore, we attempt to provide a basis upon which neural networks can be used for variable selection in statistical modeling.
62 citations
TL;DR: Probabilistic artificial neural networks are used for an approach to diagnose diabetes disease type II and training accuracy and testing accuracy of the proposed method is 89.56% and 81.49%, respectively.
Abstract: Diabetes is one of the major health problems as it causes physical disability and even death in people. Therefore, to diagnose this dangerous disease better, methods with minimum error rate must be used. Different models of artificial neural networks have the capability to diagnose this disease with minimum error. Hence, in this paper we have used probabilistic artificial neural networks for an approach to diagnose diabetes disease type II. We took advantage of Pima Indians Diabetes dataset with 768 samples in our experiments. According to this dataset, PNN is implemented in MATLAB. Furthermore, maximizing accuracy of diagnosing the Diabetes disease type II in training and testing the Pima Indians Diabetes dataset is the performance measure in this paper. Finally, we concluded that training accuracy and testing accuracy of the proposed method is 89.56% and 81.49%, respectively
51 citations
"Supervised Learning Method and Neur..." refers background in this paper
...The accuracy on training the datasets and testing it found to be 89% of training accuracy and 81% of testing accuracy respectively [4]....
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01 Jan 2011
TL;DR: This paper presents a study on the prediction of diabetes using different supervised learning algorithms of Artificial Neural Network trained using the data of 250 diabetes patients between 25 to 78 years old.
Abstract: This paper presents a study on the prediction of diabetes using different supervised learning algorithms of Artificial Neural Network. The network is trained using the data of 250 diabetes patients between 25 to 78 years old. The performance of each algorithm is further discussed through regression analysis. The prediction accuracy of the best algorithm is computed to validate accurate prediction.
41 citations
Journal Article•
TL;DR: A detailed survey is conducted on the application of different soft computing techniques for the prediction of diabetes to identify and propose an effective technique for earlier prediction of the disease.
Abstract: Neural Networks are one of the soft computing techniques that can be used to make predictions on medical data. Neural Networks are known as the Universal predictors. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. The Artificial Neural Networks (ANNs) based system can effectively applied for high blood pressure risk prediction. This improved model separates the dataset into either one of the two groups. The earlier detection using soft computing techniques help the physicians to reduce the probability of getting severe of the disease. The data set chosen for classification and experimental simulation is based on Pima Indian Diabetic Set from (UCI) Repository of Machine Learning databases. In this paper, a detailed survey is conducted on the application of different soft computing techniques for the prediction of diabetes. This survey is aimed to identify and propose an effective technique for earlier prediction of the disease.
18 citations
DOI•
01 Jan 2016
TL;DR: By combining statistical models like logistic regression model and neural networks, create a new compound that has at least error and maximum reliability and is analyzed.
Abstract: Diabetes is a common disease in the world that has not found a cure for it. Annually in our country cost a lot to care for disabilities caused by diabetes, as predicted to treatment, thus more accurately predict the condition of patients is of utmost importance and to forecasts of high precision and reliability must be accurate and to be used the reliable methods. One of these methods using artificial intelligence systems and in particular, is the use of neural networks. Given that the statistical models like logistic regression model are accurate, so in this paper, tried by combining these statistical models and neural networks, create a new compound that has at least error and maximum reliability and is analyzed. With the above suggestions model and different experiences and comparing, numerical results obtained, the accuracy and efficiency of the method has been investigated and acceptable results compared to the neural network and logistic regression methods were obtained. In this research, the criteria are the performance to minimize the error function in neural network training using a neural network in a hybrid model which eventually came to the conclusion that the error function of the neural network is equal to 0.1 and combined neural network model is equal to 0.0002.
17 citations