Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped health officials, patients, and healthcare communities to perform early detection for those diseases. Extracting the patterns from healthcare data has helped the healthcare communities to obtain complete medical data for the purpose of diagnosis. The objective of the present research work is presented to improve the surveillance detection system for chronic diseases, which is used for the protection of people’s lives. For this purpose, the proposed system has been developed to enhance the detection of chronic disease by using machine learning algorithms. The standard data related to chronic diseases have been collected from various worldwide resources. In healthcare data, special chronic diseases include ambiguous objects of the class. Therefore, the presence of ambiguous objects shows the availability of traits involving two or more classes, which reduces the accuracy of the machine learning algorithms. The novelty of the current research work lies in the assumption that demonstrates the noncrisp Rough K-means (RKM) clustering for figuring out the ambiguity in chronic disease dataset to improve the performance of the system. The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F-score, are employed to evaluate the performance of the proposed system. Furthermore, evaluation and comparison of the proposed system with the existing machine learning algorithms are presented. Finally, the proposed system has enhanced the performance of machine learning algorithms.

Doll

This chapter opens with a father in Cologne in the 1590s who snapped the arms from a crucifix and gave it to his children as a toy. Returning to the sermon by Edgeworth discussed in the preface, the chapter considers this broken object as what Edgeworth calls an “idoll”--a hybridization of doll and idoll. This possibility is linked to the wider presence of “holy dolls” in medieval Christianity, but ultimately the doll is explored not as a stable and readily identifiable category but as a way of conceiving of ambiguous objects that may be more or less human at different moments and subjected alternatingly to violence and care. The implications of this possibility are explored in relation to a medieval Christ child, a broken crucifix, and a contemporary representation of a shattered doll.

Not committing has its advantages: facilitating children's comprehension of object filler–gap dependencies is one of them

Two studies assess French-speaking children's comprehension of object filler–gap dependencies, with the goal of investigating whether the degree of specificity/set-restriction of the fronted object or the intervening subject modulates comprehension. We tease apart the predictions of various accounts attributing children's difficulties to (i) similarities between the object and the intervening subject (Gordonet al., 2001, 2004), particularly when both constituents share a structural +NP feature (Friedmannet al., 2009); (ii) increased processing cost determined by an operation of set-restriction (Goodluck 2010); and (iii) the tendency to incrementally interpret sentences and the subsequent difficulty in revising an early commitment to an agent/subject-first analysis (Trueswellet al., 1999). Our results support the incremental processing view as they reveal that only a less specific fronted object, but not a less specific intervener, enhances comprehension. This suggests that referentially ambiguous objects alleviate children from an erroneous initial interpretive commitment to an agent/subject-first structure.

Dental Numbering for Periapical Radiograph Based on Multiple Fuzzy Attribute Approach

The dental numbering for periapical radiograph based on multiple fuzzy attribute approach proposed here analyzes each individual tooth based on multiple criteria such as area/perimeter and width/height ratios. The classification and numbering in a special dental image called a periapical radiograph is studied without speculative classification in cases of ambiguous objects, so an accurate, assistive result is obtained due to the capability of handling ambiguous teeth. Experiment results in using periapical dental radiograph from the University of Indonesia indicate a total classification accuracy of 82.51%, an average classification rate per input radiograph of 84.29%, a maxilla-mandible identification accuracy from 78 radiographs of 82.05%, and a numbering accuracy from 15 radiographs of 90.47%. It is planned that the proposed classification and numbering be implemented as a submodule for dental-based personal identification now being developed.