A NOVEL APPROACH FOR MULTI VARIANT CLASSIFICATION OF MEDICAL DATA IN SHORT TEXT

Data Mining Techniques has attained its momentum in several areas, and its efficient performance in decision support has outperformed and made it a reliable choice. The medical world is one such empirical domain in which a perfect decision at right time would turn out to be a lifesaver. Medical data figures out to be majorly multi-dimensional, where relevant feature extraction is a challenging factor. Several classification approaches like SVM, Decision Trees, and Naive Based are considered to handle these profound challenges. One such challenge discussed in our paper emphasizing on Medical decision support system with Machine Learning Methodology considering diseases and treatments with their semantic relations in the document of Pub med abstracts. The proposed Multi variant classification framework aims at reducing data into attributes using PCA Transformation infusion with an efficient classification Algorithm - CNB. Our computed results are comparatively successful in attaining ultimate outcomes concerning performance metrics like Accuracy, Precision, Recall, and Time. The strength of our work lies in presenting an efficient approach for elevating enhanced decisions in Health care

A Novel Approach for Multi Variant Classification of Medical Data in Short Text

457-462Data Mining Techniques has attained its momentum in several areas, and its efficient performance in decision support has outperformed and made it a reliable choice. The medical world is one such empirical domain in which a perfect decision at right time would turn out to be a lifesaver. Medical data figures out to be majorly multi-dimensional, where relevant feature extraction is a challenging factor. Several classification approaches like SVM, Decision Trees, and Naive Based are considered to handle these profound challenges. One such challenge discussed in our paper emphasizing on Medical decision support system with Machine Learning (ML) Methodology considering diseases and treatments with their semantic relations in the document of Pub med abstracts. The proposed Multi variant classification framework aims at reducing data into attributes using PCA Transformation infusion with an efficient classification Algorithm - CNB. Our computed results are comparatively successful in attaining ultimate outcomes concerning performance metrics like Accuracy, Precision, Recall, and Time. The strength of our work lies in presenting an efficient approach for elevating enhanced decisions in Health care

Application of machine learning in predicting frailty syndrome in patients with heart failure

Prevention and diagnosis of frailty syndrome (FS) in patients with heart failure (HF) require innovative systems to help medical personnel tailor and optimize their treatment and care. Traditional methods of diagnosing FS in patients could be more satisfactory. Healthcare personnel in clinical settings use a combination of tests and self-reporting to diagnose patients and those at risk of frailty, which is time-consuming and costly. Modern medicine uses artificial intelligence (AI) to study the physical and psychosocial domains of frailty in cardiac patients with HF. This paper aims to present the potential of using the AI approach, emphasizing machine learning (ML) in predicting frailty in patients with HF. Our team reviewed the literature on ML applications for FS and reviewed frailty measurements applied to modern clinical practice. Our approach analysis resulted in recommendations of ML algorithms for predicting frailty in patients. We also present the exemplary application of ML for FS in patients with HF based on the Tilburg Frailty Indicator (TFI) questionnaire, taking into account psychosocial variables

Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques

[EN] The purpose of this study was to evaluate texture-based muscle ultrasound image analysis for the assessment and risk prediction of frailty phenotype. This retrospective study of prospectively acquired data included 101 participants who underwent ultrasound scanning of the anterior thigh. Participants were subdivided according to frailty phenotype and were followed up for two years. Primary and secondary outcome measures were death and comorbidity, respectively. Forty-three texture features were computed from the rectus femoris and the vastus intermedius muscles using statistical methods. Model performance was evaluated by computing the area under the receiver operating characteristic curve (AUC) while outcome prediction was evaluated using regression analysis. Models developed achieved a moderate to good AUC (0.67 <= AUC <= 0.79) for categorizing frailty. The stepwise multiple logistic regression analysis demonstrated that they correctly classified 70-87% of the cases. The models were associated with increased comorbidity (0.01 <= p <= 0.18) and were predictive of death for pre-frail and frail participants (0.001 <= p <= 0.016). In conclusion, texture analysis can be useful to identify frailty and assess risk prediction (i.e. mortality) using texture features extracted from muscle ultrasound images in combination with a machine learning approach.This work was supported by the following grants: Grant PID2020-113839RB-I00 funded by MCIN/AEI/10.13039/501100011033 to C.B. DM acknowledges financial support from the Conselleria d ' Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (grants AEST/2018/021 and AEST/2019/037) .Mirón-Mombiela, R.; Ruiz-España, S.; Moratal, D.; Borrás, C. (2023). Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques. Mechanisms of Ageing and Development. 215. https://doi.org/10.1016/j.mad.2023.11186021

Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome

Abstract Background Increasing life expectancy results in more elderly people struggling with age related diseases and functional conditions. This poses huge challenges towards establishing new approaches for maintaining health at a higher age. An important aspect for age related deterioration of the general patient condition is frailty. The frailty syndrome is associated with a high risk for falls, hospitalization, disability, and finally increased mortality. Using predictive data mining enables the discovery of potential risk factors and can be used as clinical decision support system, which provides the medical doctor with information on the probable clinical patient outcome. This enables the professional to react promptly and to avert likely adverse events in advance. Methods Medical data of 474 study participants containing 284 health related parameters, including questionnaire answers, blood parameters and vital parameters from the Toledo Study for Healthy Aging (TSHA) was used. Binary classification models were built in order to distinguish between frail and non-frail study subjects. Results Using the available TSHA data and the discovered potential predictors, it was possible to design, develop and evaluate a variety of different predictive models for the frailty syndrome. The best performing model was the support vector machine (SVM, 78.31%). Moreover, a methodology was developed, making it possible to explore and to use incomplete medical data and further identify potential predictors and enable interpretability. Conclusions This work demonstrates that it is feasible to use incomplete, imbalanced medical data for the development of a predictive model for the frailty syndrome. Moreover, potential predictive factors have been discovered, which were clinically approved by the clinicians. Future work will improve prediction accuracy, especially with regard to separating the group of frail patients into frail and pre-frail ones and analyze the differences among them