Anthropometry is a Greek word that consists of the two words “Anthropo” meaning human species and “metery” meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and find the factors affecting these diseases and the extent of the impact of each to make the necessary planning. We have focused on cohort studies for 10047 qualified participants from Ravansar County. Machine learning provides opportunities to improve discrimination through the analysis of complex interactions between broad variables. Among the chronic diseases in this cohort study, we have used three deep neural network models for diagnosis and prognosis of the risk of type 2 diabetes mellitus (T2DM) as a case study. Usually in Artificial Intelligence for medicine tasks, Imbalanced data is an important issue in learning and ignoring that leads to false evaluation results. Also, the accuracy evaluation criterion was not appropriate for this task, because a simple model that is labeling all samples negatively has high accuracy. So, the evaluation criteria of precession, recall, AUC, and AUPRC were considered. Then, the importance of variables in general was examined to determine which features are more important in the risk of T2DM. Finally, personality feature was added, in which individual feature importance was examined. Performing by Shapley Values, the model is tuned for each patient so that it can be used for prognosis of T2DM risk for that patient. In this paper, we have focused and implemented a full pipeline of Data Creation, Data Preprocessing, Handling Imbalanced Data, Deep Learning model, true Evaluation method, Feature Importance and Individual Feature Importance. Through the results, the pipeline demonstrated competence in improving the Diagnosis and Prognosis the risk of T2DM with personalization capability.
Background: Familial Hypercholesterolemia (FH) is a genetic disorder in lipoprotein metabolism caused by mutations that increase LDL and total cholesterol levels. High LDL and cholesterol levels increase atherosclerosis risk. FH mutations impact the LDL receptor (LDLR) gene, apolipoprotein B, and PCSK9. About 20% of FH cases have a polygenic basis that affects LDL levels. We decided to conduct a systematic review of the available research in this field to provide a thorough genes/proteins network meta-analysis on the impact of drug combinations on the management of heterozygous Familial Hypercholesterolemia (HeFH). This paper reviews and analyzes the literature on the effects of medication combinations on HeFH management. This study investigates articles that analyzed the management and adjuvants of HeFH to recommend forceful drug combinations. Methods: This systematic review and network meta-analysis analyzed the Science Direct, Embase, Scopus, PubMed, Web of Science (ISI), and Google Scholar databases without a lower time limit and up to July 2022. The current study consists of three fundamental stages. Firstly, drug combinations are recommended by reinforcement learning. In the second stage, we used a systematic review to analyze RL's outcomes in diverse populations (with a variety of ages, sex, etc.). Natural Language Processing (NLP) employs context to search these articles. We contrasted manual and NLP-based searches and discovered that NLP could find articles based on MeSH, not simply words. In stage three, we analyze RL outcomes using network meta-analysis. Results: This study uses the RAIN method to investigate the most effective medication combination for managing Heterozygous Familial Hypercholesterolemia (HeFH). Results from the method indicate that the best-recommended scenario is 2.7 times more efficient than the prescription of Ezetimibe as the initial scenario. Conclusion: Our systematic review and network meta-analysis review indicate that a drug combination of Ezetimibe, Pravastatin, and Simvastatin is highly effective. However, additional high-quality clinical trials are required to determine the efficacy and safety of other treatments.
Facial Action Coding System consists of 44 action units (AUs) and more than 7000 combinations. Hidden Markov models (HMMs) classifier has been used successfully to recognize facial action units (AUs) and expressions due to its ability to deal with AU dynamics. However, a separate HMM is necessary for each single AU and each AU combination. Since combinations of AU numbering in thousands, a more efficient method will be needed. In this paper an accurate real-time sequence-based system for representation and recognition of facial AUs is presented. Our system has the following characteristics: 1) employing a mixture of HMMs and neural network, we develop a novel accurate classifier, which can deal with AU dynamics, recognize subtle changes, and it is also robust to intensity variations, 2) although we use an HMM for each single AU only, by employing a neural network we can recognize each single and combination AU, and 3) using both geometric and appearance-based features, and applying efficient dimension reduction techniques, our system is robust to illumination changes and it can represent the temporal information involved in formation of the facial expressions. Extensive experiments on Cohn-Kanade database show the superiority of the proposed method, in comparison with other classifiers.
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