Visual explanations: Images and quantities, evidence and narrative

Automated classification of multi-class sleep stages classification using polysomnography signals: a nine- layer 1D-convolution neural network approach

The world of banking requires a marketer to be able to reduce the risk of borrowing by keeping his customers from occurring non-performing loans. One way to reduce this risk is by using data mining techniques. Data mining provides a powerful technique for finding meaningful and useful information from large amounts of data by way of classification. The classification algorithm that can be used to handle imbalance problems can use the Random Forest (RF) algorithm. However, several references state that an optimization algorithm is needed to improve the classification results of the RF algorithm. Optimization of the RF algorithm can be done using Bagging and Genetic Algorithm (GA). This study aims to classify Bank Marketing data in the form of loan application receipts, which data is taken from the www.data.world site. Classification is carried out using the RF algorithm to obtain a predictive model for loan application acceptance with optimal accuracy. This study will also compare the use of optimization in the RF algorithm with Bagging and Genetic Algorithms. Based on the tests that have been done, the results show that the most optimal performance of the classification of Bank Marketing data is by using the RF algorithm with an accuracy of 88.30%, AUC (+) of 0.500 and AUC (-) of 0.000. The optimization of Bagging and Genetic Algorithm has not been able to improve the performance of the RF algorithm for classification of Bank Marketing data.

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Analisis Perbandingan Algoritma Optimasi pada Random Forest untuk Klasifikasi Data Bank Marketing

Manual inspection of sleep staging is one of the critical challenges for the last few years because it is a time-consuming process, laborious task, and also gives a heavy burden to the sleep experts for manipulating the entire sleep recording, which alternatively affects their performance. To overcome this challenging issue, we propose an efficient approach to improve the sleep staging accuracy and multi-class classification for sleep behavior analysis using a One-dimensional Convolutional Neural Network (1D-CNN) model with the inputs of polysomnography signal. Methods: The proposed system considers the base model as 1D-CNN model because of its effectiveness, robust and high accuracy to automatically classify the sleep stages from the different medical-conditioned subjects. The proposed system has used stacking ensemble learning techniques for training and classification to improve the sleep staging accuracy for multi-class classification problems and make the system more robust and accurate predictions. Results: The proposed model has been validated on the SHHS dataset using 10-fold cross-validation techniques. This experimental results for the entire fold have been reported for the 1D-CNN for five to two sleep states classification problems. It has been found that the results of the proposed model are incomparable to the earlier state-of-the-art works in terms of classification accuracy. The proposed model is effective for clinical trials during the diagnosis of different types of sleep related disorders, and it becomes helpful to the sleep experts during the diagnosis process.

Automated Sleep Staging Classification System Based On Convolutional Neural Network Using Polysomnography Signals

Manual inspection of sleep staging is one of the critical challenges for the last few years because it is a time-consuming process, laborious task, and also gives a heavy burden to the sleep experts for manipulating the entire sleep recording, which alternatively affects their performance. To overcome this challenging issue, we propose an efficient approach to improve the sleep staging accuracy and multi-class classification for sleep behavior analysis using a One-dimensional Convolutional Neural Network (1D-CNN) model with the inputs of polysomnography signal. Methods: The proposed system considers the base model as 1D-CNN model because of its effectiveness, robust and high accuracy to automatically classify the sleep stages from the different medical-conditioned subjects. The proposed system has used stacking ensemble learning techniques for training and classification to improve the sleep staging accuracy for multi-class classification problems and make the system more robust and accurate predictions. Results: The proposed model has been validated on the SHHS dataset using 10-fold cross-validation techniques. This experimental results for the entire fold have been reported for the 1D-CNN for five to two sleep states classification problems. It has been found that the results of the proposed model are incomparable to the earlier state-of-the-art works in terms of classification accuracy. The proposed model is effective for clinical trials during the diagnosis of different types of sleep related disorders, and it becomes helpful to the sleep experts during the diagnosis process. 

Accurate diagnose of diseases prior to their treatment is a challenging task for the modern research, therefore it becomes necessary and important to use modern computing techniques to design an efficient and accurate prediction systems. Thyroid is one of the most common diseases found in human body with many side effects the accuracy for thyroid diagnosis system may be greatly improved by considering an ensemble algorithm technique. In this paper, an effective and accurate thyroid disease prediction model is developed using an ensemble of Bagging with J45 and ensemble of Bagging with SimpleCart to extract useful information and diagnose diseases. The performances of the two ensemble model were compared with single classifiers. The Bagging ensemble algorithm for thyroid prediction system promises excellent overall accuracy of 99.66% while other single selected classifiers like Bagging and SimpleCART has accuracy of 99.55% and J48 with accuracy of 99.60%. General Terms Machine Learning.

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Effective and Accurate Bootstrap Aggregating (Bagging) Ensemble Algorithm Model for Prediction and Classification of Hypothyroid Disease

Exploration of patients medical record have necessitated the need for tracking customers with adverse drug reactions so as to deliver an analysis on risk involved and which cause of action proved effective. This paper acquires customer insights on adverse reactions experienced by taking anti-malaria drugs without medical diagnosis in north eastern part of Nigeria. The data collected were computed using k-means clustering algorithm implemented on Excel Visual Basic for Applications (VBA) Macro. Cluster values generated from the program was plotted. The graph predicts age of customers who are at risk of buying drugs without proper diagnosis and medical examination. Result obtained shows that about 38.47% of working population are at risk of direct-drug prescription. The result implies that, there is tendency of low productivity and inefficiency among 38.47% of the working force.

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Francisca N. Ogwueleka

Papers

Effective and Accurate Bootstrap Aggregating (Bagging) Ensemble Algorithm Model for Prediction and Classification of Hypothyroid Disease

Predicting Risk of Direct-to-Customer Drug Prescription using K-Mean Clustering Technique