Rule Extraction in Diagnosis of Vertebral Column Disease

Computer aided diagnosis systems are getting importance in recent years. In this paper i worked on vertebral column disease (Disc Hernia, Spondylolisthesis) biomedical Dataset for disease diagnosis. i have results with a performance comparison among some of the best data mining techniques (classifying and clustering). Different type of classification algorithms gives very promising results (more than %90 accuracy) in disease diagnosis. DOI: 10.17762/ijritcc2321-8169.15035

Sparse Reject Option Classifier Using Successive Linear Programming

In this paper, we propose an approach for learning sparse reject option classifiers using double ramp loss $L_{dr}$. We use DC programming to find the risk minimizer. The algorithm solves a sequence of linear programs to learn the reject option classifier. We show that the loss $L_{dr}$ is Fisher consistent. We also show that the excess risk of loss $L_d$ is upper bounded by the excess risk of $L_{dr}$. We derive the generalization error bounds for the proposed approach. We show the effectiveness of the proposed approach by experimenting it on several real world datasets. The proposed approach not only performs comparable to the state of the art but it also successfully learns sparse classifiers

Implementasi Algoritma Genetika pada k-nearest neighbours untuk Klasifikasi Kerusakan Tulang Belakang

Abstrak Kerusakan tulang belakang dialami oleh sekitar dua pertiga orang dewasa serta termasuk ke dalam penyakit yang paling umum kedua setelah sakit kepala. Klasifikasi gangguan tulang belakang sulit dilakukan karena membutuhkan radiologist untuk menganalisa citra Magnetic Resonance Imaging (MRI). Penggunaan Computer Aided Diagnosis (CAD) System dapat membantu radiologist untuk mendeteksi kelainan pada tulang belakang dengan lebih optimal. Dataset vertebral column memiliki tiga kelas sebagai klasifikasi penyakit kerusakan tulang belakang yaitu, herniated disk, spondylolisthesis dan kelas normal yang diambil berdasarkan hasil ekstraksi citra MRI. Dataset akan diolah dalam lima eksperimen berdasarkan validasi dataset menggunakan split validation dengan pembagian data training dan data testing yang bervariasi. Pada penelitian ini diusulkan implementasi algoritma genetika pada algoritma k-nearest neighbours untuk meningkatkan akurasi dari klasifikasi gangguan tulang belakang. Algoritma genetika digunakan untuk fitur seleksi dan optimasi parameter algoritma k-nearest neighbours. Hasil penelitian menunjukan bahwa metode yang diusulkan menghasilkan peningkatan yang signifikan dalam klasifikasi kerusakan pada tulang belakang. Metode yang diusulkan menghasilkan rata-rata akurasi sebesar 93% dari lima eksperimen. Hasil ini lebih baik dari algoritma k-nearest neighbours yang menghasilkan rata-rata akurasi hanya sebesar 82.54%.   Kata kunci: algoritma genetika, k-nearest neighbours, kerusakan tulang belakang, vertebral   Abstract Spinal disorder is experienced by about two-thirds of adults and is included in the second most common disease after headaches. Classification of spinal disorders is difficult because it requires a radiologist to analyze Magnetic Resonance Imaging (MRI) images. The use of Computer Aided Diagnosis (CAD) System can help radiologists to detect abnormalities in the spine more optimally. The vertebral column dataset has three classes as a classification of spinal disorders, namely, herniated disk, spondylolisthesis and normal classes taken based on MRI Image extraction. The dataset will be processed in five experiments based on dataset validation using split validation with various training data and testing data. In this study proposed the implementation of genetic algorithms in the k-nearest neighbors algorithm to improve the accuracy of the classification of spinal disorders. Genetic algorithms are used for algorithm feature selection and parameter optimization of k-nearest neighbors. The results showed that the proposed method produced a significant increase in the classification of spinal disorder. The proposed method produces an average accuracy of 93% from five experiments. This result is better than the k-nearest neighbors algorithm which produces an average accuracy of only 82.54%.   Keywords: genetic algorithm, k-nearest neighbours, spinal disorder, vertebral column

Clasificación de patologías presentes en la columna vertebral mediante técnicas de máquinas de aprendizaje

Introduction: This paper shows the result of research entitled “Study of pathologies present in vertebral column using artificial Intelligence Techniques as support of diagnostic processes”, developed in University of Valle between the years 2016 and 2017. Problem: Studies and analyzes that are carried out on the health conditions of human beings are often invasive, which leads to greater issues. Objective: To provide a method of study from biomechanical attributes of human beings for the detection of pathologies present in vertebral column. Methodology: The study was based on testing three pattern recognition techniques, Bayes as a classic recognition technique, and intelligent techniques such as Radial Basis Functions Neural Networks (rbf), Support Vector Machines (svm) and Probabilistic Neural Networks (pnn). Results: During the classification process of the pathologies to study, the best results were obtained using pnn, while the other ones presented good classification results for a particular pathology. Conclusion: It was proven that study techniques contributes important characteristics to diagnosis processes of pathologies present in the vertebral column, such as disk hernia and spondylolisthesis. Originality: This study was carried out with information from real patients, providing study techniques and important results on the diagnosis of vertebral column pathologies.  Limitations: The study of vertebral column pathologies requires more information about the biomechanical attributes of human beings.Introducción: el artículo es resultado de la investigación “Estudio de patologías presentes en la columna vertebral empleando técnicas de inteligencia artificial como apoyo a los procesos de diagnóstico”, desarrollada en la Universidad del Valle entre 2016 y 2017. Problema: con frecuencia, los estudios y análisis que a menudo se realizan a las afecciones de salud en seres humanos con frecuencia son invasivos, lo cual conlleva problemas mayores. Objetivo: aportar un método de estudio a partir de los atributos biomecánicos de seres humanos para la detección de patologías que se presentan en la columna vertebral. Metodología: el trabajo se fundamentó en probar tres técnicas de reconocimiento de patrones; Bayes como técnica clásica de reconocimiento; y técnicas inteligentes como las redes neuronales de base radial (rbf), máquinas de soporte vectorial (svm) y redes neuronales probabilísticas (pnn). Resultados: durante el proceso de clasificación de las patologías a tratar, la que mejores resultados aportó fue la técnica de pnn, mientras que las demás presentaron buenos resultados de clasificación para una patología en particular. Conclusión: se comprobó que la aplicación de estas técnicas de estudio aporta características importantes a los procesos de diagnóstico de patologías presentes en la columna vertebral, tales como hernia discal y espondilolistesis. Originalidad: este trabajo se realizó con información de pacientes reales, y presenta técnicas de estudio y resultados importantes sobre el diagnóstico de patologías de columna vertebral. Limitaciones: el estudio de patologías de columna vertebral requiere tener más información sobre los atributos biomecánicos de los seres humanos

Patents in the computer-aided diagnosis industry

Computer aided diagnosis is a relatively new field, through the use of new techniques algorithms and technologies, it can help technicians perform a better and faster analysis, reduce or even substitute part of their workload. Patents are windows into a company's technological assets, as well as into the state of a certain technology field. In this thesis we analyzed patents that are mainly related to the automated analysis of human retinopathies. Using patent search engines we explored the various patent databases, using keywords related to the area and the international patent classification to refine the search and eliminate unrelated results, proceeding then to a thorough analysis of the dataset. By analyzing the structured and unstructured text, contained in the obtained patents, different observations where made: major players in the field,patent timelines, main technologies involved and the direction of the technology evolution

Bayesian Ensemble of Regression Trees for Multinomial Probit and Quantile Regression

This dissertation proposes multinomial probit Bayesian additive regression trees (MPBART), ordered multiclass Bayesian additive classification trees (O-MBACT) and Bayesian quantile additive regression trees (BayesQArt) as extensions of BART - Bayesian additive regression trees for tackling multinomial choice, multiclass classification, ordinal regression and quantile regression problems. The proposed models exhibit very good predictive performances. In particular, ranking among the top performing procedures when non-linear relationships exist between the response and the predictors. The proposed procedures can readily be applied on data sets with the number of predictors larger than the number of observations. MPBART is sufficiently flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives and it can also be used as a general multiclass classification procedure. Through two simulation studies and four real data examples, we show that MPBART exhibits very good out-ofsample predictive performance in comparison to other discrete choice and multiclass classification methods. To implement MPBART, the R package mpbart is freely available from CRAN repositories. When ordered gradation is exhibited by a multinomial response, ordinal regression is an appealing framework. Ensemble of trees models, while widely used for binary classification, multiclass classification and continuous response regression, have not been extensively applied to solve ordinal regression problems. This work fills this void with Bayesian sum of regression trees. The predictive performance of our ordered Bayesian ensemble of trees model is illustrated through simulation studies and real data applications. Ensemble of regression trees have become popular statistical tools for the estimation of conditional mean given a set of predictors. However, quantile regression trees and their ensembles have not yet garnered much attention despite the increasing popularity of the linear quantile regression model. This work proposes a Bayesian quantile additive regression trees model that shows very good predictive performance illustrated using simulation studies and real data applications. Further extension to tackle binary classification problems is also considered

Confronto della funzionalità meccanica dei sistemi per fusione vertebrale anteriore “AxiaLif” e “Vision Cage” per mezzo di modelli agli elementi finiti

La colonna vertebrale è la struttura portante del corpo umano, che ne permette il movimento. In ogni momento della vita di una persona, il rachide viene sottoposto a una compressione pari quasi alla metà del peso del corpo, portando nel corso degli anni ad un inevitabile degrado della capacità di assorbire e distribuire uniformemente questo peso. Complici anche i movimenti non fisiologici, il rachide va incontro a delle patologie strutturali come degenerazione discale, spondilolistesi e, più frequentemente, conseguenze algesiche come forti e cronici dolori alla schiena. Per risolvere queste patologie, laddove le terapie conservative falliscono, si interviene chirurgicamente con la gold standard delle procedure: la fusione o stabilizzazione intervertebrale. Questa tecnica chirurgica generalmente prevede la fissazione posteriore con viti e barre e molto spesso è completata con la fusione anteriore tramite cage intervertebrali o con una vite assiale. In questo lavoro di tesi si analizzano tensioni, deformazioni e spostamenti subiti dalle parti biologiche che maggiormente sono coinvolte in queste procedure, ovvero L5 e la prima vertebra del sacro. Si confronta le due metodologie di fusione anteriore spinale nel worst scenario, ovvero senza la fissazione spinale posteriore. Si sono implementati due modelli agli elementi finiti in grado di simulare il comportamento meccanico del peso fisiologico in condizioni di osteointegrazione, sia per la cage Vision e sia per la vite AxiaLIF, i due sistemi di fusione vertebrale anteriore analizzati in questo studio. Le componenti ossee del modello sono state ricavate con la tecnica CTtoFEM, mentre i due dispositivi sono stati progettati al CAD. Nel contesto analizzato e con un carico applicato equivalente a poco più di quello fisiologico, i dispositivi non hanno recato danno al tessuto osseo circostante. Poiché non è stata rilevata una netta differenza tra i due dispositivi, per poter discriminare l’uno dall’altro è doveroso compiere altre analisi, come per esempio una simulazione di spondilolistesi, sbandamenti laterali o movimenti di torsione. Questo studio, se aumentato di complessità e confrontato con ulteriori analisi complementari come quelle sopra descritte, sarà in grado di determinare preintervento, quale sarà l’impianto più adatto al quadro clinico di ogni specifico soggetto. Con una visione più ottimistica, ma ad oggi non così lontana, si potrebbero utilizzare queste analisi come processi di progettazione e produzione di dispositivi clinici subject specific