Applying Artificial Intelligence to Medical Data

Machine learning, data mining, and deep learning has become the methodology of choice for analyzing medical data and images. In this study, we implemented three different machine learning techniques to medical data and image analysis. Our first study was to implement different log base entropy for a decision tree algorithm. Our results suggested that using a higher log base for the dataset with mostly categorical attributes with three or more categories for each attribute can obtain a higher accuracy. For the second study, we analyzed mental health data tuning the parameters of the decision tree (splitting method, depth and entropy). Our results identified the most crucial attributes for the dataset. The final study is on the Kimia Path24 image dataset. We built and trained a deep convolutional neural network and tested different hypotheses of batch size, number of epoch and learning rate. For the final study, all the hypotheses were supported with our experimental results

An Application of Data Mining of Mental Health Data

Data mining lies at the interface of statistics, pattern recognition, and machine learning. An organized collection of data and proper data visualization are the main prerequisites of data mining. Proper use of data mining techniques will help to identify important patterns and relationships in a dataset. In this paper, we implement a data mining algorithm on mental health data and find the most important attributes that trigger issues with mental health treatment. For this study we used Microsoft Excel for data preparation and filtering, SQL server as the data storage, and SQL Server Analysis Service (SSAS) for building the data mining model. This is an important process which can help organizations provide a comfortable environment to employees that facing issues with mental health treatment

Improving the Predictive Performance of the c4.5 Decision Tree Algorithm for Categorical Medical Data

Work published in Southwest Decision Sciences Institute (SWDSI) 2020 Proceedings

Deep Learning: An Empirical Study on Kimia Path24

Deep learning has a large interest in medical image analysis as studies have shown several machine learning algorithms were successful in predicting disease. However, more work in needed to better understand the batch size, epoch, and learning rates. An empirical study of image processing with deep learning was conducted on the KIMIA path24 dataset. The rotation, width shifting, height shifting shear range, horizontal flip, and fill mode was used. The network was trained and validated by a total of 22,591 images from the KIMIA path24 dataset. ReLU was used for the convolution layer and softmax for the fully connected layer. Results found the batch size is inversely proportional to the network accuracy, the accuracy of a deep learning network is directly proportional to the number of epochs it passes through, and the learning rate does not bring any change to the network. The network performs best within a preferred learning rate