Excessive daytime sleepiness due to brain tumor

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early. Manual prediction is very expensive and time-consuming. Therefore, automated prediction techniques are essential. In this research, we propose an ensemble automated prediction approach that uses four machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The C-NMC leukemia dataset is used from the Kaggle repository to predict leukemia. Dataset is divided into two classes cancer and healthy cells. We perform data preprocessing steps, such as the first images being cropped using minimum and maximum points. Feature extraction is performed to extract the feature using pre-trained Convolutional Neural Network-based Deep Neural Network (DNN) architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF) as feature Selection techniques. Classification machine learning algorithms and ensemble voting are applied to selected features. Results reveal that SVM with 90.0% accuracy outperforms compared to other algorithms.

Prevalence of sleep disturbances and their effects on quality of life in adults with untreated pituitary tumor and meningioma

PURPOSE

To explore the prevalence of sleep disturbances and their effects on quality of life in adults with pituitary tumor or meningioma.

METHODS

This prospective study included 33 and 44 patients with pituitary tumor and meningioma, respectively. All participants completed a series of valid questionnaires for assessing sleep and quality of life; all participants wore 3-day actigraph prior to related treatment. The actigraph-derived sleep parameters included total sleep time, sleep onset latency, wake after sleep onset, sleep efficiency, and dichotomy index (I < O) value.

RESULTS

The prevalence of insomnia, excessive daytime sleepiness, and poor sleep quality was 46.8%, 6.5%, and 81.8%, respectively. The differences in these sleep parameters between patients with pituitary tumor and those with meningioma were nonsignificant. Only 27 participants completed the actigraphic assessments. The mean I < O value was 95.99%, and nearly 60% participants exhibited circadian rhythm disruption. Sleep quality was the only sleep variable independently correlated with preoperative quality of life, even after adjustments for confounders (B = 0.80, p = 0.02).

CONCLUSIONS

Insomnia, poor sleep quality, and disrupted circadian rhythm are highly prevalent in adults with untreated pituitary tumor or meningioma. Sleep quality independently correlated with quality of life. We indirectly confirmed that tumor location may not be a possible cause of sleep changes.