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Heart diseases have become one of the most causes of death among Egyptians. With 500 deaths per 100,000 occurring annually in Egypt, it has been noticed that medical data faces a high-dimensional problem that leads to a decrease in the classification accuracy of heart data. So the purpose of this study is to improve the classification accuracy of heart disease data for helping doctors efficiently diagnose heart disease by using a hybrid classification technique.

This paper used a new approach based on the integration between dimensionality reduction techniques as multiple correspondence analysis (MCA) and principal component analysis (PCA) with fuzzy c means (FCM) then with both of multilayer perceptron (MLP) and radial basis function networks (RBFN) which separate patients into different categories based on their diagnosis results in this paper, a comparative study of the performance performed including six structures such as MLP, RBFN, MLP via FCM–MCA, MLP via FCM–PCA, RBFN via FCM–MCA and RBFN via FCM–PCA to reach to the best classifier.

The results show that the MLP via FCM–MCA classifier structure has the highest ratio of classification accuracy and has the best performance superior to other methods; and that Smoking was the most factor causing heart disease.

This paper shows the importance of integrating statistical methods in increasing the classification accuracy of heart disease data.

The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.

The approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).

The proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.

This work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.

Purpose – This chapter presents a case for reframing medical sociology to focus on diagnosis as a pivotal category of analysis via an extended literature review of the diagnosis as a tool of medicine.

Methodology/approach – Conceptual overview.

Practical implications – By reviewing the range of social functions served by diagnosis, and the similarly wide assortment of social forces that shape diagnostic categories, this chapter pushes social scientists and theorists to consider diagnosis as a cornerstone to the understanding of health, illness, and disease.

Originality/value of paper – Building on Brown's earlier call for a sociology of diagnosis, this chapter sets forth potential parameters for this field. It defines how the study of diagnosis is dissipated across myriad areas of scholarship, including medicalization, disease theory, ethics, classification theory, and history of medicine. Extirpating diagnosis and revealing it for specific discussion provides an opportunity to study topics such as illness experiences, health social movements, and disease recognition from a different and rich perspective.

The present research work is carried out for determining haemoprotozoan diseases in cattle and breast cancer diseases in humans at early stage. The combination of LeNet and bidirectional long short-term memory (Bi-LSTM) model is used for the classification of heamoprotazoan samples into three classes such as theileriosis, babesiosis and anaplasmosis. Also, BreaKHis dataset image samples are classified into two major classes as malignant and benign. The hyperparameter optimization is used for selecting the prominent features. The main objective of this approach is to overcome the manual identification and classification of samples into different haemoprotozoan diseases in cattle. The traditional laboratory approach of identification is time-consuming and requires human expertise. The proposed methodology will help to identify and classify the heamoprotozoan disease in early stage without much of human involvement.

LeNet-based Bi-LSTM model is used for the classification of pathology images into babesiosis, anaplasmosis, theileriosis and breast images classified into malignant or benign. An optimization-based super pixel clustering algorithm is used for segmentation once the normalization of histopathology images is conducted. The edge information in the normalized images is considered for identifying the irregular shape regions of images, which are structurally meaningful. Also, it is compared with another segmentation approach circular Hough Transform (CHT). The CHT is used to separate the nuclei from non-nuclei. The Canny edge detection and gaussian filter is used for extracting the edges before sending to CHT.

The existing methods such as artificial neural network (ANN), convolution neural network (CNN), recurrent neural network (RNN), LSTM and Bi-LSTM model have been compared with the proposed hyperparameter optimization approach with LeNET and Bi-LSTM. The results obtained by the proposed hyperparameter optimization-Bi-LSTM model showed the accuracy of 98.99% when compared to existing models like Ensemble of Deep Learning Models of 95.29% and Modified ReliefF Algorithm of 95.94%.

In contrast to earlier research done using Modified ReliefF, the suggested LeNet with Bi-LSTM model, there is an improvement in accuracy, precision and F-score significantly. The real time data set is used for the heamoprotozoan disease samples. Also, for anaplasmosis and babesiosis, the second set of datasets were used which are coloured datasets obtained by adding a chemical acetone and stain.

Through the example of a “regulatory ranking” – an index produced with the aim to regulate the pharmaceutical market by pushing companies in the direction of providing greater access to medicine in developing countries – this chapter focuses on indexing and ranking as infrastructural processes which inscribe global problem spaces as unfolding actionable territories for market intervention. It foregrounds the “Indexal thinking” which structures and informs regulatory rankings – their aspiration to align the interests of different stakeholders and to entice competition among the ranked companies. The authors detail the infrastructural work through which such ambitions are enacted, detailing processes of infrastructural layering/collage and patchwork through which analysts naturalize/denaturalize various contested categories in the ranking’s territory. They reflect on the consequences of such attempts at reconfiguring global topologies for the problems these governance initiatives seek to address.

The purpose of this paper is to study the factors associated with the occurrence of diseases and beriberi among prisoners incarcerated in the two largest Remand and Correctional Facilities (RCF).

This was a cross-sectional descriptive and analytical study carried out from April 20 to May 19, 2017, in the RCFs of Ouagadougou and Bobo-Dioulasso. All prisoners who consulted and those referred to the health center by the health-care team were included in the study. Complaints and diagnosed diseases information were collected using the second version of the International Classification of Primary Care (ICPC-2). The authors used a logistic regression model to perform univariate and multivariate analyses.

Of the 1,004 prisoners from the two RCFs included in the study (32.6%), 966 (96%) were male. The median age was 31.6 years. The distribution of diseases diagnosed using the ICPC-2 showed a predominance of gastrointestinal tract, skin and respiratory tract diseases among 206 (19.3%), 188 (17.6%) and 184 (17.2%) prisoners, respectively. A total of 302 prisoners (30.1%) had clinical beriberi, and 80 prisoners (8%) were underweight. Being incarcerated for more than nine months was independently associated with a high risk of digestive and respiratory diseases as well as beriberi.

This study highlighted higher frequencies of digestive, skin and respiratory complaints and diseases in the two largest detention centers in Burkina Faso. These diseases are variously related to age, penal status and length of incarceration. In addition, underweight and thiamin vitamin deficiency responsible for beriberi are more frequent in adult prisoners, those not attending school, convicted prisoners and those with a length of stay in detention of more than nine months. These concrete results should help define a strategy and priority actions needed to reduce morbidity in prisons.

The actions should include the intervention of specialists in the field of common diseases in prisons, the improvement of individual hygiene conditions and environment, the improvement of the quality and quantity of the food ration, a strategy to reduce prison overcrowding. Other actions must be planned to allow specific groups such as women and minors to have access to health care that is adapted to them. Beyond the central concern of promoting the rights of prisoners and humanizing prisons, actions to improve the health of prisoners are part of an overall public health approach with its socio-economic and environmental implications.

There is a need for a strong commitment from the State to develop a prison health policy that prioritizes the prevention of communicable and non-communicable diseases that are particularly prevalent in this context, without forgetting mental health and nutrition. This requires a collaboration of stakeholders based on better intersectorial communication, the implementation of a monitoring and evaluation system for the health of prisoners, an enhancement of the status of health-care providers working in prisons and an increase in the funding allocated to the health of prisoners with the mobilization of the necessary funds.

This study uses a primary health care classification to assess the health of inmates in a prison in Africa. It contributes to the weak evidence around prison health surveillance and health profiling of prisoners in Africa.

Previous dynamic prediction models rarely handle multi-period data with different intervals, and the large-scale patient hospital records are not effectively used to improve the prediction performance. This paper aims to focus on the prediction of cardiovascular disease using the improved long short-term memory (LSTM) model.

A new model based on the traditional LSTM was proposed to predict cardiovascular disease. The irregular time interval is smoothed to obtain the time parameter vector, and it is used as the input of the forgetting gate of LSTM to overcome the prediction obstacle caused by the irregular time interval.

The experimental results show that the dynamic prediction model proposed in this paper obtained a significant better classification performance compared with the traditional LSTM model.

In this paper, the authors improved the LSTM by smoothing the irregular time between different medical stages of the patient to obtain the temporal feature vector.

Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the research. In order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing methodologies. Hence deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation cost.

Multifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the architecture. Classifier is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new variant. The performance of disease progression learning progress utilizes the precision of the constituent classifiers, which usually has larger generalization benefits than those optimized classifiers.

Deep learning architecture uses weight function, bias function on input layers and max pooling. Outcome of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease, and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected network. Experimental results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and accuracy.

Proposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient trajectory. Then, the generative output layer of the proposed model produces the progression outcome of the disease of the particular patient trajectory. The model tries to produce the accurate prognosis outcomes by employing data conditional probability function. The originality of the work defines 70% and comparisons of the previous methods the method of values are accurate and increased analysis of the predictions.

Informatics work introduces information professionals to taxonomies and other classification systems outside the boundaries of traditional bibliographic systems. This paper aims to provide an overview of the International Statistical Classification of Diseases and Related Health Problems (ICD) for informaticians and information professionals who may not have worked with the system previously.

In this paper, the author reviews the purpose, history, current use and future trends of the ICD classification system.

ICD is used globally as a standard vocabulary for medical diagnoses and, in the USA, for medical procedures in hospitals. Understanding the classification system is vital to working with clinical medical data.

The ICD classification system is not commonly used by information professionals. This paper provides a brief overview that will familiarize the information professional with the standard and its uses related to medical practice.

Reviews available literature on gender bias and the process of medical care. Current findings point to possible gender bias in treatment protocols for kidney and cardiac patients. Other clinical conditions have not been studied. Identifies methodological challenges to such research and discusses the need for further research.