Optimization of RFM for automated breast cancer detection

Recent years have seen an upsurge in the acceptance of illness diagnosis and prediction utilizing ML algorithms. A ML model can be employed in the diagnosis of breast cancer illness. In this research, an effective breast cancer prediction model with grid search approach is provided. Using the random forest approach, grid search is used to find the best n-estimator, which may provide the highest possible accuracy for predicting breast cancer. The accuracy of the suggested model can then be utilised to contrast its effectiveness to that of a standard RFM. The experimental result analysis demonstrates that the optimized model has 97.07 percent accuracy whereas the regular random forest technique has an accuracy of 94.73 percent in breast cancer detection

Studying the context of psychoses to improve outcomes in Ethiopia (SCOPE): Protocol paper.

BACKGROUND: Global evidence on psychosis is dominated by studies conducted in Western, high-income countries. The objectives of the Study of Context Of Psychoses to improve outcomes in Ethiopia (SCOPE) are (1) to generate rigorous evidence of psychosis experience, epidemiology and impacts in Ethiopia that will illuminate aetiological understanding and (2) inform development and testing of interventions for earlier identification and improved first contact care that are scalable, inclusive of difficult-to-reach populations and optimise recovery. METHODS: The setting is sub-cities of Addis Ababa and rural districts in south-central Ethiopia covering 1.1 million people and including rural, urban and homeless populations. SCOPE comprises (1) formative work to understand care pathways and community resources (resource mapping); examine family context and communication (ethnography); develop valid measures of family communication and personal recovery; and establish platforms for community engagement and involvement of people with lived experience; (2a) a population-based incidence study, (2b) a case-control study and (2c) a cohort study with 12 months follow-up involving 440 people with psychosis (390 rural/Addis Ababa; 50 who are homeless), 390 relatives and 390 controls. We will test hypotheses about incidence rates in rural vs. urban populations and men vs. women; potential aetiological role of khat (a commonly chewed plant with amphetamine-like properties) and traumatic exposures in psychosis; determine profiles of needs at first contact and predictors of outcome; (3) participatory workshops to develop programme theory and inform co-development of interventions, and (4) evaluation of the impact of early identification strategies on engagement with care (interrupted time series study). Findings will inform development of (5) a protocol for (5a) a feasibility cluster randomised controlled trial of interventions for people with recent-onset psychosis in rural settings and (5b) two uncontrolled pilot studies to test acceptability, feasibility of co-developed interventions in urban and homeless populations