Machine Learning Approach for Prediction of Bone Mineral Density and Fragility Fracture in Osteoporosis

Osteoporosis is a prevailing bone disease, which weakens the bone and is one of the major factors of disability, especially in elderly persons. In this thesis, we developed various machine learning models to predict fracture risk of osteoporosis. These mod- els were built to base their predictions on genotype and phenotype data of patients. We performed two dierent types of analysis: fracture risk prediction (a classica- tion model) and bone mineral density (BMD) prediction (a regression model). For fracture risk prediction we implemented four dierent algorithms: logistic regression, random forest, gradient boosting, and multi-layer perceptron (MLP) based on dier- ent risk factors identied. We performed our experiments using 307 and 1103 Single Nucleotide Polymorphism (SNPs) with data from 5133 patients. For both 307 and 1103 SNPs the performance of MLP was the best with area under curve (AUC) of 0.970 and 0.981 respectively. Logistic regression had the worst performance among four models with AUC of 0.816 and 0.904. For BMD prediction we implemented linear regression, random forest, gradient boosting and MLP and as a performance metric we plotted mean squared error (MSE) versus number of iterations for both train and test set of data. The random forest performed the best in both cases with MSE of 0.004 and linear regression was the worst with MSE of 0.104 in the test data for both sets of SNPs

A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery

Background: Low back pain is a common problem in many people. Neurosurgeons recommend posterior spinal fusion (PSF) surgery as one of the therapeutic strategies to the patients with low back pain. Due to the high risk of this type of surgery and the critical importance of making the right decision, accurate prediction of the surgical outcome is one of the main concerns for the neurosurgeons.Methods: In this study, 12 types of multi-layer perceptron (MLP) networks and 66 radial basis function (RBF) networks as the types of artificial neural network methods and a logistic regression (LR) model created and compared to predict the satisfaction with PSF surgery as one of the most well-known spinal surgeries.Results: The most important clinical and radiologic features as twenty-seven factors for 480 patients (150 males, 330 females; mean age 52.32 ± 8.39 years) were considered as the model inputs that included: age, sex, type of disorder, duration of symptoms, job, walking distance without pain (WDP), walking distance without sensory (WDS) disorders, visual analog scale (VAS) scores, Japanese Orthopaedic Association (JOA) score, diabetes, smoking, knee pain (KP), pelvic pain (PP), osteoporosis, spinal deformity and etc. The indexes such as receiver operating characteristic–area under curve (ROC-AUC), positive predictive value, negative predictive value and accuracy calculated to determine the best model. Postsurgical satisfaction was 77.5% at 6 months follow-up. The patients divided into the training, testing, and validation data sets.Conclusion: The findings showed that the MLP model performed better in comparison with RBF and LR models for prediction of PSF surgery.Keywords: Posterior spinal fusion surgery (PSF); Prediction, Surgical satisfaction; Multi-layer perceptron (MLP); Logistic regression (LR) (PDF) A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery. Available from: https://www.researchgate.net/publication/325679954_A_Predictive_Model_for_Assessment_of_Successful_Outcome_in_Posterior_Spinal_Fusion_Surgery [accessed Jul 11 2019].Peer reviewe

Predicting healthcare high-cost users using data mining methods

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe increase in healthcare costs is, perhaps, one of the most important issues that governments and organizations face nowadays. An ageing population and technological advancements are the key reasons for this phenomenon. In this scenario, proactive measures are very important. This work aimed to improve the effectiveness of the prevention by helping the identification of the most probable high-cost users of health services in future years. Data from 2015 to 2019 of approximately 30,000 Central Bank of Brazil’s Health Program’s enrollees were used to train, validate and test four types of models, considering the kind of high-cost users (simple or cost-bloomers, i.e., non-high-cost in previous periods) and the time-span between predictors and the dependent variable (none or one year), an innovation suggested by other authors. Different percentual cut-off points to define highcost were used, and up to 67% of high-risk users’ expenses could be correctly captured. Results confirmed the importance of previous costs data for this kind of prediction and showed that costbloomers and one-year time-span approaches reach good performance, creating opportunities to improve users’ health outcomes while contributing to the fiscal sustainability of private and public health systems

Neural Network Analysis of Bone Vibration Signals to Assesses Bone Density

Osteoporosis is a systemic disease, characterised by low bone mineral density (BMD) with a consequent increase in bone fragility. The most commonly used method to examine BMD is dual energy X-ray absorptiometry (DXA). However DXA cannot be used reliably in children less than 5 years old because of the limitations in the availability of required normative data. Vibration analysis is a well-established technique for analysing physical properties of materials and so it has the potential for assessing BMD. The overall purpose of this study was development and evaluation of low frequency vibration analysis as a tool to assess BMD in children. A novel portable computer-controlled system that suitably vibrated the bone, acquired, stored, displayed and analysed the resulting bone vibration responses was developed and its performance was investigated by comparing it with DXA-derived BMD values in children. 41 children aged between 7 and 15 years suspected of having abnormal BMD were enrolled. The ulna was chosen for all tests due to the ease with which it could be vibrated and responses measured. Frequency spectra of bone vibration responses were obtained using both impulse and continuous methods and these plus the participants’ clinical data were processed by a multilayer perceptron (MLP) artificial neural network. The correlation coefficient values between MLP outputs and DXA-derived BMD values were 0.79 and 0.86 for impulse and continuous vibration methods respectively. It was demonstrated that vibration analysis has potential for assessing fracture ris

Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs

Screening tests are vital for detecting diseases, especially at early stages, where efforts can prevent further illness. For example, osteoporosis is a systemic skeletal disease characterized by low bone mass and microarchitectural deterioration of bone tissue, resulting in bone fragility and susceptibility to fracture. Dual-energy x-ray absorptiometry is commonly used to diagnose osteoporosis since it evaluates bone mineral density. It is the most standard method for diagnosing osteoporosis, but it is not immediately available and is commonly used for research due to the high capital cost. Further, dual-energy x-ray absorptiometry is not used for populational-based screening due to its suboptimal ability to predict hip fractures based on measurements. Therefore, it is recommended to adopt a case-finding strategy to identify individuals at risk who benefit from the dual-energy x-ray absorptiometry examination. Several indices have been developed to estimate bone quality in dental panoramic radiographs to identify individuals at risk of osteoporosis. In particular, the mandibular cortical width index. Studies suggest that dentists can measure the mandibular cortical width to identify individuals at risk and refer them for bone mineral density testing. However, this endeavor is time-consuming and inconsistent due to the bone's unclear borders and the challenge of determining the mental foramen's position, leading to varying measurements between clinicians. Therefore, the dentistry community is investigating how to automate this process effectively and accurately. In an attempt to address some of these problems, this thesis presents a method to assess the mandibular cortical width index automatically. Four different object detectors were analyzed to determine the mental foramen's position. EfficientDet showed the highest average precision (0.30). Therefore, it was combined with an iterative procedure to estimate mandibular cortical width. The results are promising

Machine learning for brain stroke: a review

Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. Therefore, the aim of this work is to classify state-of-arts on ML techniques for brain stroke into 4 categories based on their functionalities or similarity, and then review studies of each category systematically. A total of 39 studies were identified from the results of ScienceDirect web scientific database on ML for brain stroke from the year 2007 to 2019. Support Vector Machine (SVM) is obtained as optimal models in 10 studies for stroke problems. Besides, maximum studies are found in stroke diagnosis although number for stroke treatment is least thus, it identifies a research gap for further investigation. Similarly, CT images are a frequently used dataset in stroke. Finally SVM and Random Forests are efficient techniques used under each category. The present study showcases the contribution of various ML approaches applied to brain stroke.info:eu-repo/semantics/publishedVersio

Machine Learning for Prediction of Trabecular and Cortical Bone Mineral Density

Osteoporosis becomes very common problem for people after a certain age, which results in fragility fractures without any previous symptoms. One of the primary predictors of osteoporosis is bone mineral density (BMD). BMD is the mineral content of bone, at the optimal levels, that makes the bone strong enough to bear the regular load and elastic enough to handle the irregular twisting load. Two of the major parts of the bone that help to acquire such property are trabecular and cortical bone. This thesis focuses on predicting the BMDs of trabecular and cortical bone for men. For this purpose we performed Genome Wide Association Study (GWAS) for quality control and obtained new subsets of 537 and 536 Single Nucleotide Polymorphisms (SNPs) associated with trabecular and cortical BMDs. Various machine learning algorithms were used for the predictive analysis, among which linear regression (LR), support vector machine (SVM) and multi-layer perceptron (MLP) gave much better results with the newly obtained subset of SNPs, compared to the results using the 1103 and 307 SNPs associated with BMD found in the existing literature. LR gave mean squared error (MSE) of 0.000658 and coefficient of determination (r2) of 0.643479, SVM gave MSE of 0.000628 and r2 of 0.65971, and MLP gave MSE 0.000683 and r2 0.62989 for trabecular BMD with 537 SNPs. Similarly, LR, SVM, and MLP gave MSEs of 0.001109, 0.001103, and 0.00112, and r2 of 0.707548, 0.709079 and 0.703947, respectively, for cortical BMD with 536 SNPs. In both cases, SVM gave better results

Computer aided detection of oral lesions on CT images

Oral lesions are important findings on computed tomography images. They are difficult to detect on CT images because of low contrast, arbitrary orientation of objects, complicated topology and lack of clear lines indicating lesions. In this thesis, a fully automatic method to detect oral lesions from dental CT images is proposed to identify (1) Closed boundary lesions and (2) Bone deformation lesions. Two algorithms were developed to recognize these two types of lesions, which cover most of the lesion types that can be found on CT images. The results were validated using a dataset of 52 patients. Using non training dataset, closed boundary lesion detection algorithm yielded 71% sensitivity with 0.31 false positives per patient. Moreover, bone deformation lesion detection algorithm achieved 100% sensitivity with 0.13 false positives per patient. Results suggest that, the proposed framework has the potential to be used in clinical context, and assist radiologists for better diagnosis. --Abstract, page iv

The intelligent estimating of spinal column abnormalities by using artificial neural networks and characteristics vector extracted from image processing of reflective markers

Spinal column abnormities such as kyphosis and lordosis are the most common deformity that normally compare to the standard norms. To classify the subjects into the healthy and abnormal groups based on the angle values of the standard norms, the aim of this study was to use the artificial neural network method as a standard way for realizing the spinal column abnormalities. In this way, 40 male students (26 ± 2 years old, 72 ± 2.5 kg weight, and 169 ± 5.5 cm height) volunteered for this research. The lumbar lordosis and thoracic kyphosis angles were analyzed using an image processing of 13 reflective markers set on the spines process of the thoracic and lumbar spine. Therefore, after analyzing the position of these markers, a characteristic vector was extracted from the lateral side of every subject. The artificial neural network was trained by using the characteristic vector extracted from the labeled image of that person to diagnose abnormalities. The results indicate that the high efficiency of this method as the CCR (train) and CCR (test) was about 96 and 93%, respectively. These results show that the neural network can be considered as a standard way to diagnose the spinal abnormalities. Moreover, the most important benefit of this method is the estimation of spinal column abnormalities without considering intermediate quantities, and also the standard norms of these intermediate quantities can be considered as a non-invasive method.Keywords: Abnormality, spinal column, kyphosis, lordosis, neural network, classificationAfrican Journal of Biotechnology Vol. 12(4), pp. 419-42

Classification and features selection method for obesity level prediction

Obesity has become one of the world’s largest health issues, rich and poor countries, without exception, have each year larger populations with this condition. Obesity and overweight are defined as abnormal or excessive fat accumulation that may impair health according to the World Health Organization (WHO) and has nearly tripled since 1975. Data Mining and their techniques have become a strong scientific field to analyze huge data sources and to provide new information about patterns and behaviors from the population. This study uses data mining techniques to build a model for obesity prediction, using a dataset based on a survey for college students in several countries. After cleaning and transformation of the data, a set of classification methods was implemented (Logistic Model Tree - LMT, RandomForest - RF, Multi-Layer Perceptron - MLP and Support Vector Machines - SVM), and the feature selection methods InfoGain, GainRatio, Chi-Square and Relief, finally, crossed validation was performed for the training and testing processes. The data showed than LMT had the best performance in precision, obtaining 96.65%, compared to RandomForest (95.62%), MLP (94.41%) and SMO (83.89%), so this study shows that LMT it can be used with confidence to analyze obesity and similar data