Papers by Meghana Preethi
EAI Endorsed Trans. Scalable Inf. Syst., 2021
One of the main areas where machine learning (ML) techniques are used vastly is in prediction of ... more One of the main areas where machine learning (ML) techniques are used vastly is in prediction of diseases. Diabetic neuropathy (DN) disease is a complication of diabetes which causes damage to nerves. Early prediction of DN helps diabetic patient to avoid its complications. The main aim of this work is to identify various risk factors of DN and predict it accurately using ML techniques. Radial basis function (RBF) network is an artificial neural network proposed to obtain better results than traditional ML classification techniques. CART, random forest and logistic regression are existing classification techniques considered. Accuracy, recall, f1 score, area under ROC curve (AUC), Matthews correlation coefficient (MCC) and kolmogorov-smirnov (KS) statistic are performance metrics used to evaluate and compare algorithms. From comparative study it was observed that proposed technique RBF network performed better. The performance metric values obtained for RBF network are accuracy-68.1...
EAI Endorsed Transactions on Pervasive Health and Technology, 2018
INTRODUCTION: Diabetic foot ulcer (DFU) is a complication of diabetes that affects most of the di... more INTRODUCTION: Diabetic foot ulcer (DFU) is a complication of diabetes that affects most of the diabetic patients. It will cause open wounds on the foot. Untreated DFU will lead to amputation and infection, which results in removal of foot or leg. As diabetes is the major health problem faced by people of all age groups, identifying foot ulcers at an early stage is essential. In this context, an efficient model to predict the foot ulcer accurately was proposed in this work. OBJECTIVES: To predict DFU using an effective neural network algorithm on a suitable dataset that consists of risk factors and clinical outcomes of the disease. METHODS: In recent days, ML techniques are most commonly used for predicting various diseases. To achieve the objectives a neural network technique, namely extreme learning machine (ELM) is proposed to predict DFU accurately. In addition, three existing algorithms, namely KNN, SVM with Gaussian kernel and ANN are also considered. These are implemented in R programming. RESULTS: Algorithms compared in terms of five evaluation metrics accuracy, zero-one loss, threat score/critical success index (TS/CSI), false omission rate (FOR) and false discovery rate (FDR). The values of accuracy, 0-1 loss, TS/CSI, FOR and FDR obtained for ELM are 96.15%, 0.0385, 0.95, 0 and 0.05 respectively. CONCLUSION: After comparison, it was discovered that ELM had outperformed other algorithms in terms of all the metrics. Thus, it was recommended to use ELM over other algorithms while predicting diabetic foot ulcers. Keywords: Diabetic foot ulcer, KNN, SVM with Gaussian kernel, artificial neural network (ANN), extreme learning machine (ELM), accuracy, zero-one loss, critical success index (CSI), false omission rate (FOR) and false discovery rate (FDR).
Developing Preeminent Model Based on Empirical Approach to Prognose Liver Metastasis
Smart Innovation, Systems and Technologies, 2021
ICST Transactions on Scalable Information Systems
Gestational diabetes mellitus occurs due to high glucose levels in the blood. Pregnant women are ... more Gestational diabetes mellitus occurs due to high glucose levels in the blood. Pregnant women are affected by this type of diabetes. A blood test is to be performed to identify diabetes. The Oral Glucose Tolerance Test (OGTT) is a blood test performed between the 24th and 28th week of pregnancy that is necessary to identify and overcome the side effects of GDM. The main objective of this work is to train a model by utilizing the training data, evaluate the trained model using the test data, and compare existing machine learning algorithms with a Gradient boosting machine (GBM) to achieve a better model for the effective prediction of gestational diabetes. In this work, the analysis was done with a few existing algorithms and the Extreme learning machine and Gradient boosting techniques. The k-fold cross-validation technique is applied with values of k as 3, 5, and 10 to obtain better performance. The existing algorithms implemented are the Naive Bayes classifier, Support Vector Machi...
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Papers by Meghana Preethi