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Background:The Primary Health Care Setting gives a challenging opportunity for the clinicians to deal with pregnancy into favorable outcomes solely based on the clinical skills in view of innumerable socio-cultural-economic barriers. The Pregnant women make satisfactory progress-to full term, deliver with minimal morbidity, no loss of life and healthy baby-How to ensure? This is the objectives of our study. Suppose Obstetricians spare time, use checklist, Prioritize and provide care will it make any difference in saving mothers? Objectives: Describe in detail the process of Focused Antenatal Care as practiced in Primary Health Care setting and Minimize mortality and morbidity due to pregnancy by 25 percent from 169. (Maternal Mortality Rate (MMR 169). Methods: This is a community based descriptive, prospective, cohort study about a group of pregnant women till their delivery, using multiple cluster random sampling of 251 high risk pregnant women and subsequent follow up over 3 months with focused care. Compilation of data and analysis using SPSS Version 20. Results: Total Study participants 251 represented all the sections of target population with regard to socio-economic and cultural background. The participants attended FANC giving a response rate of 100%. These participants had one or more risk factor. All but 10 participants attended 4 or more FANC clinic visits 241 (96%). In these participants the commonest manageable morbid conditions are underweight (20%), anaemia (14%) preeclampsia (8%), eclampsia (2%) and gestational diabetes. In our finding 87% mothers completed full term pregnancy, 11.5% preterm (>28 but < 37 weeks) while 1.5% Post term. There were 241 live births, 87% Baby weight > 2.5Kg, 13% Baby weight < 2.5 Kg. with an average of 2.9Kg. Conclusions:We are able to describe the Focused ANC and able to help improve the quality of life and to minimize morbidity and mortality in pregnant women.

Patterns discovered from based on collected molecular profiles of patient tumour samples, and also clinical metadata, could be used to provide personalized cancer treatment to patients with similar molecular subtypes. Computational algorithms for cancer diagnosis, prognosis, and therapeutics that can recognize specific functions and aid in classifiers based on a plethora of publicly accessible cancer research outcomes are needed. Machine learning, a branch of artificial intelligence, has a great deal of potential for problem solving in cryptic cancer datasets, as per a literature study. We focus on the new state of machine learning applications in cancer research in this study, illustrating trends and analysing major accomplishments, roadblocks, and challenges along the way to clinic implementation. In the context of noninvasive treating cancer using diet-based and natural biomarkers, we propose a novel machine learning algorithm.