A Context Aware Decision-Making Algorithm for Human-Centric Analytics: Algorithm Development and Use Cases for Health Informatics System

Healthcare analytics indeed plays a crucial role in leveraging data from various sources to identify trends, patterns, and insights that can lead to improvements in healthcare delivery and decision-making. Feature selection is particularly important in healthcare analytics because it helps identify the most relevant data attributes or features that contribute to predictive models or analysis. By selecting the most informative features, healthcare professionals can build more accurate models and gain better insights into patient outcomes, treatment effectiveness, disease prediction, and more. Challenges in healthcare data include issues related to data quality, privacy concerns, data integration from disparate sources, and the complexity of healthcare systems. Overcoming these challenges requires robust analytics techniques and methodologies tailored to the healthcare domain. Machine learning algorithms play a significant role in healthcare analytics by enabling predictive modeling, clustering, classification, and other tasks. Choosing the right algorithm depends on the specific healthcare application and the nature of the data being analyzed. This chapter outlines Feature Selection algorithms and discusses the challenges associated with healthcare data. It also introduces an abstract architecture for data analytics in the healthcare domain. Furthermore, it compares and categorizes various machine learning algorithms and techniques according to their applications in healthcare analytics.

Wearable devices focused on fitness, safety, and data monitoring have become increasingly common. It is not surprising, given that a sedentary lifestyle can lead to various health issues such as weight gain, chronic and acute illnesses, and reduced productivity in daily life. Researchers from both business and academic settings have delved deeply into monitoring fitness, examining aspects like sleep patterns, cardiac health, activity levels, overall well-being, and recovery from illness. They employ a range of techniques including deep learning, machine learning, and statistical methods to analyze the data collected by these wearables. This study aims to gather data from Fitbit Inspire HR and Fitbit Versa using the Fitbit API and to investigate any anomalies, trends, and correlations within the collected healthcare data. The data is classified using the K-means algorithm based on various parameters of the Fitbit healthcare data. 

Gallstone disease is a prevalent chronic condition impacting individuals worldwide, posing significant challenges to healthcare systems globally. It ranks among the most common ailments encountered by individuals seeking emergency care due to abdominal discomfort. The complexity of gallbladder ultrasound scans arises from numerous factors, including variations in gallbladder anatomy. In this study, we propose a healthcare informatics system aimed at identifying and analyzing gallstones. We conduct a thorough examination of several state-of-the-art object detection algorithms, including Faster Region-based Convolutional Neural Network (Faster RCNN), Mask Region-based Convolutional Neural Network (Mask R-CNN), and Single Shot Detector (SSD) Our approach, which combines elements of Mask R-CNN, SSD, and Faster R-CNN, facilitates the precise detection of gallstones within the gallbladder by leveraging region-based proposals. We specifically focus on training the Mask RCNN model with various backbone networks. Ultrasound images utilized in our experiments were sourced from medical professionals, encompassing diverse demographic characteristics such as gender, age, and urban/rural residence. Our findings demonstrate that the Mask R-CNN model, with a Resnet-101-FPN backbone network, excels in gallstone detection, surpassing alternative techniques in object localization accuracy.

Diabetic retinopathy (DR) is the main cause of blindness in diabetic patients. Early and accurate diagnosis can improve the analysis and prognosis of the disease. One of the earliest symptoms of DR is hemorrhages in the retina. Therefore, we propose a new method for accurate hemorrhage detection from retinal fundus images. Here, the proposed method uses the modified contrast enhancement method to improve the edge details from the input retinal fundus images. In the second stage, a convolutional neural network (CNN) with improved LSTM based on hybrid Harris Hawks with Mayfly (HHMO) is proposed to detect and classify the hemorrhages. Finally, the proposed CNN with HHO-LSTM is compared with the existing techniques including machine learning and deep learning techniques such as Naïve Bayes, SVM, ANN, etc., and traditional CNN, LSTM, and other techniques, respectively. Therefore, the comparison can prove that the proposed model is more effective in detecting and classifying Hemorrhages in the retina due to diabetic retinopathy. The performance metrics considered in this work are accuracy, specificity, sensitivity, f1-score, precision, etc.