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Polycystic ovary syndrome (PCOS) is of clinical and public health importance as it is very common, affecting up to one in five women of reproductive age. It has significant and diverse clinical implications including reproductive (infertility, hyperandrogenism, hirsutism), metabolic (insulin resistance, impaired glucose tolerance, type 2 diabetes mellitus, adverse cardiovascular risk profiles) and psychological features (increased anxiety, depression and worsened quality of life). Obesity worsens the presentation of PCOS and weight management (weight loss, maintenance or prevention of excess weight gain) is proposed as an initial treatment strategy, best achieved through lifestyle changes incorporating diet, scientific exercises and behavioral interventions.

Noninvasive assessment of severity of liver fibrosis is crucial for understanding histology and making decisions on antiviral treatment for chronic HBV in view of the associated risks of biopsy. aimed to develop a computer-assisted assessment system for the evaluation of liver disease severity by using machine leaning classifier based on physical-layer with serum markers. The retrospective data set, including 920 patients, was used to establish Decision Tree classifier (DTC), Random Forest Classifier (RFC), Logistic Regression Classifier (LRC), and Support Vector Classifier (SVC) for liver fibrosis severity assessment. Training and testing samples account for 50% of the data set, respectively. The best indicator combinations were selected in random combinations of 24 indicators including 67 108 760 group indicators by four different machine learning classier. The resulting classifiers prospectively tested in 50% testing patients, and the sensitivity, septicity, overall accuracy, and receiver operating characteristics (ROC) were used to compare four classifiers to existed 19 models. Results show that the RFC-based classifier system, with 9 indicators, is feasible to assess severity for liver fibrosis with diagnostic accuracy (greater than 0.83) superior to existing 19 models. Additional studies based on a large data set with full serum markers and imaging information are necessary to enhance diagnostic accuracy and to expand clinical application.

In world economy agriculture has own major part to improve financial stability of a country. However still we are facing many loopholes in that to develop agricultural techniques and to adopt new things to get better results. Hence still agriculture is the developing industry in many countries. Due to these problems farmers are not getting good crop productivity. To overcome from these problems first we have to predict crop yield by using latest technologies. For this here we have to use Machine Learning technology to predict crop yield from a dataset which is already stored in ( SMLT ) to get the details like Uni-variate analysis, By-vairiate analysis, Multi-vairiate analysis, Missing value treatment, Variable identification etc. To determine which algorithms are more accurate to predict crop yield, A comparative study done between Machine Learning algorithms. The best outcome shows that the effectiveness of the proposed Machine learning algorithm technique can be compared with best accuracy with calculation, precision, Recall, F1 score.