Epidemiological breast cancer prediction by country: A novel machine learning approach.

Breast cancer remains a significant contributor to cancer-related deaths among women globally. We seek for this study to examine the correlation between the incidence rates of breast cancer and newly identified risk factors. Additionally, we aim to utilize machine learning models to predict breast cancer incidence at a country level. Following an extensive review of the available literature, we have identified a range of recently studied risk factors associated with breast cancer. Subsequently, we gathered data on these factors and breast cancer incidence rates from numerous online sources encompassing 151 countries. To evaluate the relationship between these factors and breast cancer incidence, we assessed the normality of the data and conducted Spearman's correlation test. Furthermore, we refined six regression models to forecast future breast cancer incidence rates. Our findings indicate that the incidence of breast cancer is most positively correlated with the average age of women in a country, as well as factors such as meat consumption, CO2 emissions, depression, sugar consumption, tobacco use, milk intake, mobile cells, alcohol consumption, pesticides, and oral contraceptive use. As for prediction, the CatBoost Regressor successfully predicted future breast cancer incidence with an R squared value of 0.84 ± 0.03. An increased incidence of breast cancer is mainly associated with dietary habits and lifestyle. Our findings and recommendations can serve as a baseline for developing educational programs intended to heighten awareness amongst women in countries with heightened risk.

Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based Models.

PURPOSE: Selection of appropriate adjuvant therapy to ultimately reduce the risk of breast cancer (BC) recurrence is a challenge for medical oncologists. Several automated risk prediction models have been developed using retrospective clinical data and have evolved significantly over the years in terms of predictors of recurrence, data usage, and predictive techniques (statistical/machine learning [ML]). METHODS: Following PRISMA guidelines, we performed a systematic literature review of the aforementioned statistical and ML models published between January 2008 and December 2022 through searching five digital databases-PubMed, ScienceDirect, Scopus, Cochrane, and Web of Science. The comprehensive search yielded a total of 163 papers and after a screening process focusing on papers that dealt exclusively with statistical/ML methods, only 23 papers were deemed appropriate for further analysis. We benchmarked the studies on the basis of development, evaluation metrics, and validation strategy with an added emphasis on racial diversity of patients included in the studies. RESULTS: In total, 30.4% of the included studies use statistical techniques, while 69.6% are ML-based. Among these, traditional ML models (support vector machines, decision tree, logistic regression, and naïve Bayes) are the most frequently used (26.1%) along with deep learning (26.1%). Deep learning and ensemble learning provide the most accurate predictions (AUC = 0.94 each). CONCLUSION: ML-based prediction models exhibit outstanding performance, yet their practical applicability might be hindered by limited interpretability and reduced generalization. Moreover, predictive models for BC recurrence often focus on limited variables related to tumor, treatment, molecular, and clinical features. Imbalanced classes and the lack of open-source data sets impede model development and validation. Furthermore, existing models predominantly overlook African and Middle Eastern populations, as they are trained and validated mainly on Caucasian and Asian patients.

Practical Acoustic Energy-Based Estimation of Inhalation Flow Rate for Asthma Monitoring.

The estimation of inhalation flow rate (IFR) using acoustic devices has recently received attention. While existing work often assumes that the microphone is placed at a fixed distance from the acoustic device, this assumption does not hold in real settings. This leads to poor estimation of the IFR since the received acoustic energy varies significantly with the distance. Despite the fact that the acoustic source is passive and only one microphone is used, we show in this paper that the distance can be estimated by exploiting the inhaler actuation sound, generated when releasing the medication. Indeed, this sound is used as a reference acoustic signal which is leveraged to estimate the distance in real settings. The resulting IFR estimation is shown to be highly accurate (R2 = 80.3%).