Classification of Cardiotocography Data with WEKA

Cardiotocography (CTG) records fetal heart rate (FHR) and uterine contractions (UC) simultaneously. Cardiotocography trace patterns help doctors to understand the state of the fetus. Even after the introduction of cardiotocograph, the capacity to predict is still inaccurate. This paper evaluates some commonly used classification methods using WEKA. Precision,Recall, F-Measrue and ROC curve have been used as the metric to evaluate the performance of classifiers. As opposed to some of the earlier research works that were unable to identify Suspicious and Pathologic patterns, the results obtained from the study in this paper could precisely identify pathologic and Suspicious cases. Best results were obtained from J48, Random Forest and Classification via Regression

Classification of Cardiotocography Data with WEKA

Cardiotocography (CTG) records fetal heart rate (FHR) and uterine contractions (UC) simultaneously. Cardiotocography trace patterns help doctors to understand the state of the fetus. Even after the introduction of cardiotocograph, the capacity to predict is still inaccurate. This paper evaluates some commonly used classification methods using WEKA. Precision,Recall, F-Measrue and ROC curve have been used as the metric to evaluate the performance of classifiers. As opposed to some of the earlier research works that were unable to identify Suspicious and Pathologic patterns, the results obtained from the study in this paper could precisely identify pathologic and Suspicious cases. Best results were obtained from J48, Random Forest and Classification via Regression

Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.Comment: Machine Learning for Healthcare 201

Agent-Based Middleware for Web Service Dynamic Integration on Peer-to-Peer Networks

Performing business processes normally requires dynamic and casual collaborations among enterprises that are not centrally controlled. Enterprise application integration and Peer-to-Peer computing provide a solution to the business processes. Web service integration and agent-based middleware constitute the paradigms to implement enterprise application integration on Peer-to-Peer networks because of the loosely-coupled property of Web services and the autonomous, adaptive, and interactive properties of agents. Previous Web service integration researches focused on Web service integration based on client/server network architecture. However, along with the highly demands of Web service quality, Web services are not always provided on client/server networks. This research aims to construct an agent-based middleware for Web service dynamic integration on Peer-to-Peer networks to pursue the integration of optimal quality of Web services for enterprise application integration. The paper introduces the agent-based middleware including the formalization, Web service dynamic integration model, agent reasoning model, and the application.10 page(s

Minimum Weight - Minimum Degree Spanning Tree Problem

: We consider the problem of constructing a spanning tree for a graph G = (V, E) with n vertices and having two constraints: (i) maximal degree should be the smallest among all spanning trees of G (ii) it should also have the minimum weight. This problem is referred to as the MW-MD spanning tree problem which is easily shown to be NP-hard. We present a few approximate algorithms to solve this problem. Keywords: Approximate algorithms, complexity, graph algorithms, spanning trees, minimum degree spanning trees, minimum spanning trees, matchings. 1. Introduction This paper presents three approximate solutions to the minimum weight - minimum degree spanning tree problem. The construction of constrained spanning trees of graphs has been studied before by several researchers, see for example [5,9,11]. The minimum spanning tree problem has been solved efficiently both in the sequential and parallel cases [10]. However, it is worthwhile to note that even simple constraints on the structure..

A Single Phase Protocol for Total and Causal Ordering of Group Operations in Distributed Systems

The total and causal ordering of group operations in distributed systems is at the core of solutions to many problems in distributed computing. Existing approaches are based on either a centralized coordinator to assign a sequence number for each operation or on a distributed two-phase protocol to determine the total ordering. This paper proposes a distributed single-phase protocol which determines the total ordering of group operations at the moment when operations are generated, without the need for a second phase of determining the sequence number. Therefore, the latency time can be kept in the range of [0; d], where d is the maximum message propagation delay between any pair of sites in the system. Furthermore, in contrast to the arbitrary total ordering imposed by two-phase protocols, the total ordering imposed by the proposed protocol is consistent with the causal ordering among group operations, which is required by many distributed applications. With an optimized acknowledging..