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To detect breast cancer in the early stages, microcalcifications are considered a key symptom. Several scientific investigations were performed to fight against this disease for which machine learning techniques can be extensively used. Particle swarm optimization (PSO) is recognized as one among several efficient and promising approach for diagnosing breast cancer by assisting medical experts for timely and apt treatment. This paper uses weighted particle swarm optimization (WPSO) approach for extracting textural features from the segmented mammogram image for classifying microcalcifications as normal, benign, or malignant, thereby improving the accuracy. In the breast region, tumor part is extracted using optimization methods. Here, artificial intelligence (AI) is proposed for detecting breast cancer, which reduces the manual overheads. AI framework is constructed for extracting features efficiently. This designed model detects the cancer regions in mammogram (MG) images and rapidly classifies those regions as normal or abnormal. This model uses MG images obtained from hospitals.

Dengue fever is a worldwide issue, especially in Yemen. Although early detection is critical to reducing dengue disease deaths, accurate dengue diagnosis requires a long time due to the numerous clinical examinations. Thus, this issue necessitates the development of a new diagnostic schema. The objective of this work is to develop a diagnostic model for the earlier diagnosis of dengue disease using Efficient Machine Learning Techniques (EMLT). This paper proposed prediction models for dengue disease based on EMLT. Five different efficient machine learning models, including K-Nearest Neighbor (KNN), Gradient Boosting Classifier (GBC), Extra Tree Classifier (ETC), eXtreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM). All classifiers are trained and tested on the dataset using 10-Fold Cross-Validation and Holdout Cross-Validation approaches. On a test set, all models were evaluated using different metrics: accuracy, F1-sore, Recall, Precision, AUC, and operating time. Based on the findings, the ETC model achieved the highest accuracy in Hold-out and 10-fold cross-validation, with 99.12 % and 99.03 %, respectively. In the Holdout cross-validation approach, we conclude that the best classifier with high accuracy is ETC, which achieved 99.12 %. Finally, the experimental results indicate that classifier performance in holdout cross-validation outperforms 10-fold cross-validation. Accordingly, the proposed dengue prediction system demonstrates its efficacy and effectiveness in assisting doctors in accurately predicting dengue disease.

TB infection is a global problem, especially in Yemen. Early detection is critical to reducing TB deaths. As a result, accurate tuberculosis diagnosis takes time due to numerous clinical examinations. This problem requires a new diagnosis schema. In this study, we proposed classification models based on Efficient Machine Learning Techniques (EMLT), which predict whether the patient is TB-positive or TB-negative. Nine Different Efficient Machine learning models were trained and tested in two imbalance dataset cases using Stratified 10-Fold Cross-Validation and Holdout Cross-Validation and balanced dataset case using Holdout Cross-Validation with Synthetic Minority Oversampling Technique (SMOTE). The best model was evaluated on a test set using F1-score measure in imbalanced dataset case and accuracy measure in balanced dataset case. Based on the obtained results, the models that achieved the highest value of the F1-Score measure in the imbalanced dataset were LR and GBC with 99.826% value in Stratified Cross-Validation approach and GBC with 86.0334 in the Holdout Cross-Validation approach. And the models that achieved the highest value of the accuracy measure in the balanced dataset case (SMOTE) and Holdout Cross-Validation, were LR and GBC with a 99.725% value.

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