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Background Tuberculosis (TB) remains one of the top infectious killers in the world and a prominent fatal disease in developing countries. This study proposes a prototypical solution to early prevention of TB based on its primary symptoms, signs, and risk factors, implemented by means of machine learning (ML) predictive algorithms. Further novelty of the study lies in the uniqueness of patient dataset collected from three top-ranked hospitals of Sindh, Pakistan, via a self-administered survey patient-records that comprises a set of questions asked by the doctors treating TB patients in real-time. A total of 1,200 survey patient-records were evenly distributed among all three hospitals, viz. ICT Kotri, LUMHS Jamshoro, and Civil Hospital Hyderabad. Methods To develop the required prototypes, the research made use of five distinct benchmark ML algorithms: decision tree (DT), Gaussian naive Bayes (GNB), logistic regression classifier (LRC), adaptive boosting (AdaBoost), and neural network (NN), whose performance was evaluated by considering various performance metrics, i.e., accuracy, precision, recall, F1 score, and confusion matrix. Results The experimental results, graphically visualized and systematically discoursed, demonstrate that early detection of TB classifiers, including DT, GNB, LRC, AdaBoost, and NN, attained accuracy rates of 92.11%, 89.04%, 90.35%, 93.42%, and 92.98%, respectively. These results indicate effective diagnosis of TB disease by each implemented ML algorithm.

Breast cancer (BrC) stands as the most prevalent cancer affecting women globally, comprising 24.5% of all female cancer diagnoses and contributing to 15.0% of total cancer-related fatalities. The timely detection and precise categorization of breast cancer play pivotal roles in enhancing patient prognosis and treatment outcomes. The main goal is to enhance the precision of classifying mammogram images, thus offering vital support to radiology experts in diagnosing BrCs. The proposed model encompasses several pivotal stages, including pre-processing, feature extraction, segmentation, and classification. To assess the model's efficacy, we employed the INBreast dataset. During pre-processing, mammogram images were enhanced through a customized contrast-limited adaptive histogram equalization (mCLAHE) technique coupled with data augmentation. Segmentation was executed utilizing the Res-SegNet model, and feature extraction employing the VGG-19 model. The classification was conducted via a stacked autoencoder (SAE) with particle swarm optimization (PSO). Our proposed model exhibited notably high performance compared to alternative models such as CNN, Yolo-v4, and Inception-v3. The results unveiled an accuracy of 98.33%, precision of 99.39%, recall of 98.78%, specificity of 93.75%, an F1-score of 99.08%, and an MCC score of 90.04%.

The adoption of emerging technologies in healthcare systems plays a crucial part in anti-obesity initiatives. COVID-19 has intensified the Body Mass Index (BMI) discourses in AI (Artificial Intelligence)-powered social media. However, few studies have reported on the influence of digital content on obesity prevention policies. Understanding the nature and forums of obese metaphors in social media is the first step in policy intervention. The purpose of this paper is to understand the mutual influence between obesity and COVID-19 and determine its policy implications. This paper analyzes the public responses to obesity using Twitter data collected during the COVID-19 pandemic. The emotional nature of tweets is analyzed using the NRC lexicon. The results show that COVID-19 significantly influences perceptions of obesity; this indicates that existing public health policies must be revisited. The study findings delineate prerequisites for obese disease control programs. This paper provides policy recommendations for improving social media interventions in health service delivery in order to prevent obesity.

Abstract This paper presents a novel secure medical image transmission scheme using hybrid visual cryptography and Hill cipher (HVCHC) between sender and receiver. The gray scale medical images have been considered as a secret image and split into different shares by visual cryptography (VC) encryption process. The split shares are once again encoded by Hill cipher (HC) encode process for improving the efficiency of the proposed method. In this process, the encrypted medical image (shares) pixels are converted as characters based on the character determination (CD) and lookup tables. In result, a secret image is converted into characters. These characters are sent to the receiver/authenticated person for the reconstruction process. In receiver side, the ciphertext has been decoded by HC decode process for reconstructing the shares. The reconstructed shares are decrypted by the VC decryption process for retaining the original secret medical image. The proposed algorithm has provided better CC, less execution time, higher confidentiality, integrity, and authentication (CIA). Therefore, using this proposed method, cent percent of the original secret medical image can be obtained and the secret image can be prevented from the interception of intruders/third parties.