Diabetes is currently one of the most common, dangerous, and costly diseases globally caused by increased blood sugar or a decrease in insulin in the body. Diabetes can have detrimental effects on people’s health if diagnosed late. Today, diabetes has become one of the challenges for health and government officials. Prevention is a priority, and taking care of people’s health without compromising their comfort is an essential need. In this study, the ensemble training methodology based on genetic algorithms was used to diagnose and predict the outcomes of diabetes mellitus accurately. This study uses the experimental data, actual data on Indian diabetics on the University of California website. Current developments in ICT, such as the Internet of Things, machine learning, and data mining, allow us to provide health strategies with more intelligent capabilities to accurately predict the outcomes of the disease in daily life and the hospital and prevent the progression of this disease and its many complications. The results show the high performance of the proposed method in diagnosing the disease, which has reached 98.8%, and 99% accuracy in this study.

Background: Metastasis is the main cause of death toll among breast cancer patients. Since current approaches for diagnosis of lymph node metastases are time-consuming, deep learning (DL) algorithms with more speed and accuracy are explored for effective alternatives. Methods: A total of 220025 whole-slide pictures from patients’ lymph nodes were classified into two cohorts: testing and training. For metastatic cancer identification, we employed hybrid convolutional network models. The performance of our diagnostic system was verified using 57458 unlabeled images that utilized criteria that included accuracy, sensitivity, specificity, and P-value. Results: The DL-based system that was automatically and exclusively capable of quantifying and identifying metastatic lymph nodes was engineered. Quantification was made with 98.84% accuracy. Moreover, the precision of VGG16 and Recall was 92.42% and 91.25%, respectively. Further experiments demonstrated that metastatic cancer differentiation levels could influence the recognition performance. Conclusion: Our engineered diagnostic complex showed an elevated level of precision and efficiency for lymph node diagnosis. Our innovative DL-based system has a potential to simplify pathological screening for metastasis in breast cancer patients.

Background: Breast cancer (BC) is a prevalent disease and a major cause of mortality among women worldwide. A substantial number of BC patients experience metastasis which in turn leads to treatment failure and death. The survival rate has been significantly increased due to more rapid detection and substantial improvements in adjuvant therapies including newer chemotherapeutic and targeted agents, and better radiotherapy techniques.Methods: In this study, we cross-compared the application of advanced artificial intelligence algorithms such as Logistic Regression, K-Nearest Neighbors, Discrete Cosine Transform, Random Forest Classifier, Support Vector Machines, Multilayer Perceptron, and Ensemble to diagnose BC metastasis. We further combined MLP with genetic algorithm (GA) as a hybrid method of intelligent analysis. The core data we used for comparison belonged to the images of both benign and malignant tumors collected from Wisconsin Breast Cancer dataset from the UCI repository.Results: The application of several different algorithms to the collection of BC data indicated that these algorithms have comparable accuracy rate in detecting and predicting cancer. However, our hybrid algorithm showed superior accuracy, sensitivity and specificity compared to the individual algorithms. Two methods of comparison (Cross-Validation and Holdout) were applied to this study which produced consistent results.Conclusion: Our findings indicate that our MLP-GA hybrid algorithm can speed up diagnosis with higher accuracy rate than the individual patterns of algorithm.

Heart disease is one of the most complicated diseases, and it affects a large number of individuals throughout the world. In healthcare, particularly cardiology, early and accurate detection of cardiac disease is critical. The Heart Disease Data Set-UCI repository collects data on heart disease. The search space and complexity of the classification models are increased by this raw dataset, which contains redundant and inconsistent data. We need to eliminate the redundant and unnecessary elements from the data to improve classification accuracy. As a consequence, feature selection approaches might be useful for reducing the cost of diagnosis by identifying the most important qualities. This research developed an ensemble classification model based on a feature selection approach in which selected features play a role in classification. Accordingly, a classification approach was introduced using ensemble learning with a genetic algorithm, feature selection, and biomedical test values to diagnose heart disease. Based on the results, it is deduced that the benefits of using the feature selection method vary depending on the utilized machine learning technique. However, the best-proposed model based on the combination of genetic algorithm and the ensemble learning model has achieved an accuracy of 97.57% on the considered datasets. The suggested diagnosis system achieved better accuracy than previously proposed methods and can easily be implemented in healthcare to identify heart disease. Graphical abstract

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We examined lysosomal participation in the degradation of tumor cells from human breast biopsies, utilizing the histochemical activity and localization of acid phosphatase (AP). Enzyme activities in benign and malignant lesions were compared. AP was faintly detected in normal mammary epithelia and was marked in malignant cells. The histochemical patterns of AP distribution in the breast tissues showed differences between normal and neoplastic cells. AP staining was more intense in ductal carcinoma in situ (DCIS) associated with invasive ductal carcinoma (IDC) cases compared to when IDC was present alone. These results suggest that non-invasive breast cancers, such as DCIS, may be subjected to more lysosomal cellular lysis than is observed in invasive breast cancers, like IDC. Keywords acid phosphatase, lysosomes, breast cancer, ductal carcinoma in situ, invasive ductal carcinoma

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The area of deep learning research (DL) has seen significant growth in recent years and has shown incredible promise for AI in medical applications. In this approach, we have proposed a deep neural network framework for the categorization of feature-based diabetes data. The dataset is classified using the SoftMax layer after features are collected from it using the wrapper approach. Additionally, the network is adjusted through supervised grid research with the training dataset. However, due to the risk of misdiagnosis in a medical context, we evaluated our model using measures such as precision, recall, specificity, and F1 score and obtained superior results. The proposed architecture has been tested using Diabetes 130-US hospital data, which consists of 100,000 patient files with an average of 55 attributes. As well, five machine learning algorithms—Nave Bayes, Random Forests, Decision Trees, Support vector machines, and Ensemble learning—are used to compare results from experiments. The classification accuracy of the ML algorithm was 86%. This research suggests a Deep Learning system based on feature selection for classifying diabetes data. This method has been tested on UCI machine learning data and has outperformed several other classification techniques, reaching 85.61% accuracy in this study.