An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions

HYPOTHYROID DISEASE ANALYSIS BY USING MACHINE LEARNING

Thyroid illness frequently manifests as hypothyroidism. It is evident that people with hypothyroidism are primarily female. Because the majority of people are unaware of the illness, it is quickly becoming more serious. It is crucial to catch it early on so that medical professionals can treat it more effectively and prevent it from getting worse. Machine learning illness prediction is a challenging task. Disease prediction is aided greatly by machine learning. Once more, unique feature selection strategies have made the process of disease assumption and prediction easier. To properly monitor and cure this illness, accurate detection is essential. In order to build models that can forecast the development of hypothyroidism. In this project, we utilized machine learning approaches such Logistic Regression, Decision Trees, and Naive Bayes. Here we used thyroid function-related measures and characteristics from a UCI Machine Learning Repository dataset. The main goals were to properly assess each machine learning model\u27s performance and fine-tune its hyperparameters. With an accuracy rate of 99.87%, the findings of this study generated the model\u27s ability to predict hypothyroidism were pretty remarkable. This high degree of accuracy shows how useful these machine learning algorithms are as diagnostic v and therapeutic tools for hypothyroid patients early on. This experiment demonstrates the potential of machine learning in healthcare and has an impact on diagnosis. It is crucial that you do this appropriately

Optimising use of electronic health records to describe the presentation of rheumatoid arthritis in primary care: a strategy for developing code lists

Background Research using electronic health records (EHRs) relies heavily on coded clinical data. Due to variation in coding practices, it can be difficult to aggregate the codes for a condition in order to define cases. This paper describes a methodology to develop ‘indicator markers’ found in patients with early rheumatoid arthritis (RA); these are a broader range of codes which may allow a probabilistic case definition to use in cases where no diagnostic code is yet recorded. Methods We examined EHRs of 5,843 patients in the General Practice Research Database, aged ≥30y, with a first coded diagnosis of RA between 2005 and 2008. Lists of indicator markers for RA were developed initially by panels of clinicians drawing up code-lists and then modified based on scrutiny of available data. The prevalence of indicator markers, and their temporal relationship to RA codes, was examined in patients from 3y before to 14d after recorded RA diagnosis. Findings Indicator markers were common throughout EHRs of RA patients, with 83.5% having 2 or more markers. 34% of patients received a disease-specific prescription before RA was coded; 42% had a referral to rheumatology, and 63% had a test for rheumatoid factor. 65% had at least one joint symptom or sign recorded and in 44% this was at least 6-months before recorded RA diagnosis. Conclusion Indicator markers of RA may be valuable for case definition in cases which do not yet have a diagnostic code. The clinical diagnosis of RA is likely to occur some months before it is coded, shown by markers frequently occurring ≥6 months before recorded diagnosis. It is difficult to differentiate delay in diagnosis from delay in recording. Information concealed in free text may be required for the accurate identification of patients and to assess the quality of care in general practice

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions

Can clinicians and scientists explain and prevent unexplained underperformance syndrome in elite athletes: an interdisciplinary perspective and 2016 update

The coach and interdisciplinary sports science and medicine team strive to continually progress the athlete's performance year on year. In structuring training programmes, coaches and scientists plan distinct periods of progressive overload coupled with recovery for anticipated performances to be delivered on fixed dates of competition in the calendar year. Peaking at major championships is a challenge, and training capacity highly individualised, with fine margins between the training dose necessary for adaptation and that which elicits maladaptation at the elite level. As such, optimising adaptation is key to effective preparation. Notably, however, many factors (eg, health, nutrition, sleep, training experience, psychosocial factors) play an essential part in moderating the processes of adaptation to exercise and environmental stressors, for example, heat, altitude; processes which can often fail or be limited. In the UK, the term unexplained underperformance syndrome (UUPS) has been adopted, in contrast to the more commonly referenced term overtraining syndrome, to describe a significant episode of underperformance with persistent fatigue, that is, maladaptation. This construct, UUPS, reflects the complexity of the syndrome, the multifactorial aetiology, and that ‘overtraining’ or an imbalance between training load and recovery may not be the primary cause for underperformance. UUPS draws on the distinction that a decline in performance represents the universal feature. In our review, we provide a practitioner-focused perspective, proposing that causative factors can be identified and UUPS explained, through an interdisciplinary approach (ie, medicine, nutrition, physiology, psychology) to sports science and medicine delivery, monitoring, and data interpretation and analysis

Catalan Cancer Plan 2022-2026

Càncer; Planificació sanitària; CatalunyaCancer; Health planning; CataloniaCáncer; Planificación sanitaria; CataluñaEn aquest document es presenta una avaluació de l’impacte del càncer a Catalunya fins al 2025 (any triat per seguir la metodologia i els períodes temporals de l’Agència Internacional de Recerca sobre el Càncer, IARC). Tot seguit, es presenta una avaluació dels avenços introduïts en el període 2015-2020, així com dels reptes pendents, sobretot considerant els objectius introduïts pel Pla Europeu contra el Càncer (Europe’s Beating Cancer Plan), publicat el febrer del 2021 amb motiu del Dia Mundial del Càncer. Finalment, es presenten els objectius per al proper període de forma sintètica

Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences

Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients’ disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning models and high dimensional data sources such as electronic health records, magnetic resonance imaging scans, cardiotocograms, etc. These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years because it addresses the interpretability and trust concerns of critical decision makers, including those in clinical and medical practice. Methods In this work, we focus on AdaBoost, a black box model that has been widely adopted in the CAD literature. We address the challenge – to explain AdaBoost classification – with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, Adaptive-Weighted High Importance Path Snippets (Ada-WHIPS), makes use of AdaBoost’s adaptive classifier weights. Using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes of the internal decision trees of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model’s decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure stability that is better suited to the XAI setting. Results Experiments on 9 CAD-related data sets showed that Ada-WHIPS explanations consistently generalise better (mean coverage 15%-68%) than the state of the art while remaining competitive for specificity (mean precision 80%-99%). A very small trade-off in specificity is shown to guard against over-fitting which is a known problem in the state of the art methods. Conclusions The experimental results demonstrate the benefits of using our novel algorithm for explaining CAD AdaBoost classifiers widely found in the literature. Our tightly coupled, AdaBoost-specific approach outperforms model-agnostic explanation methods and should be considered by practitioners looking for an XAI solution for this class of models