International Conference on Advances in Computing and Technology (ICACT)

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    Decision Support for Diagnosing Thyroid Diseases Using Machine Learning
    (3rd International Conference on Advances in Computing and Technology (ICACT ‒ 2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Jayamini, W.K.D.; Weerasinghe, H.D.
    Diagnosis of thyroid disorders using two machine learning techniques was studied in this research. Multilayer Perceptron Neural Network with Back-propagation algorithm and Random Forest algorithm were the two algorithms used to build the models for classifying the thyroid diagnosis classes; Hyperthyroidism, Hypothyroidism, Normal. Models were developed with different structures by changing the relevant parameters and the outcomes of the developed models were compared with each other. For developing different neural networks, parameters such as the number of hidden layers, number of neurons in hidden layers and learning rates were changed. For developing different random forest models, parameters such as the number of features per tree and the number of trees in forest were changed. Those models were trained and tested using two different datasets of thyroid diagnosis (Dataset 1 and Dataset 2) which have different attributes that are related to diagnosing thyroid diseases. The models were tested using 10-fold cross-validation while the models were compared and evaluated using the measures Accuracy (%), Mean Absolute Error, Root Mean Squared Error, TP rate, FP rate, Precision and Recall. In diagnosing thyroid disease, both the algorithms performed well. Multilayer Perceptron Neural Network with Backpropagation algorithm performed well for Dataset 1 with an accuracy of 96.7442% and Random Forest algorithm performed well for the Dataset 2 with a mean accuracy level of 98.4915%.
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    Introducing Novel Classification Methodology to Detect Kidney Disease Patterns in Sri Lanka
    (3rd International Conference on Advances in Computing and Technology (ICACT ‒ 2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Chathurangi, K.A.A.; Kapila, R.M.; Rathnayaka, T.
    The healthcare sector has vast amount of medical data which are not properly analyzed and mined to discover useful information and interesting patterns. Applying data mining techniques on such domain can help medical practitioners to predict even the crucial diseases with ease. This study introduced a novel kidney disease classification methodology in Sri Lankan domain using data mining techniques. Basically there are two types of kidney diseases that can be found in Sri Lanka namely Chronic Kidney Disease (CKD) and Acute Kidney Disease (AKD). The aim of this work is building a model to predict whether a person has a risk on having a kidney disease or not and a model for CKD prediction. The data collected from 108 patients are used to train and test the models. Random Forest algorithm and a multilayered feed forward neural network were used to build the models. Result of this study is a modified Artificial Neural Network with 2 hidden layers to detect kidney disease which gives 0.80952 accuracy and a model with the combination of Random Forest algorithm and Artificial Neural Network with 3 hidden layers for CKD prediction which gives 0.81395 accuracy for testing data. The constructed models give high accuracy and minimum error rate when comparing with the other data mining algorithms.