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The classification of recurrence and non recurrence of Hepato Cellular carcinoma (HCC) outcome after Radio Frequency Ablation therapy is a critical task. Multiple time series clinical liver cancer dataset is collected from different dataset and time interval. A merging algorithm is used to merge all attributes collected from different sources in multiple time periods. In order to preserve the originality of information, statistical measures of each attribute is calculated and considered them as additional attributes for accurate prediction. However the merged dataset is unbalanced, in which, the number of samples from HCC recurrence class is much smaller than from HCC non recurrence. The feature weighting scheme select optimal features and parameter of classifiers are sequentially obtained from multiple iterations which causes higher computation time. In this paper, an efficient sampling approach is proposed using Inverse Random under Sampling (IRUS) to overcome class imbalance issue. IRUS under sample the majority class which creates a number of distinct partitions with a boundary separated minority and majority class samples. Additionally an optimization approach is proposed using Artificial Plant Optimization (APO) algorithm to select optimal features and parameters of classifiers to improve the effectiveness and efficiency of classification. The optimization approach reduces the number of iteration and computation time for feature selection and parameter selection for classifiers which classify the recurrence and non recurrence of HCC. Classify patients with HCC and without HCC based on optimal features and parameters by Support Vector Machine (SVM) and Random Forest(RF) classifiers. Finally, the experimental results are conducted to prove the effectiveness of the proposed method over existing method in terms of accuracy, specificity, sensitivity and balanced accuracy. [ABSTRACT FROM AUTHOR]

BACKGROUND: Hypertension is highly prevalent in India but frequency of resistant hypertension has not been well studied. These patients are prone to high risk of hypertensive complications. The objective of our study was to evaluate the prevalence of resistant hypertension patients among the hypertensive population. METHODS: The study was carried out among the hypertensive patients in tertiary care hospital in India. Patient data, characteristics and class of drugs taking was recorded using a pre-coded questionnaire. Morisky questionnaire were used to assess the self medication adherence. Chi-square test for association was used to analyse the statistical difference between the hypertensive and resistant hypertension patients and related variables. RESULT: A total of 200 patients were included in the study, In the 35% (n=70) of patients have essential hypertension and 65% (n=130) of patients have resistant hypertension. Resistant hypertension was more significantly associated with patients ≥40 years of age, who are all have normal BMI, not doing their physical activity, non compliant to recommended diet and not taking their antihypertensive medication regularly. Prevalence of comorbid condition, including diabetes mellitus (p=0.000), hyperlipidemia (p=0.000), cardiovascular disease(p=0.000),chronic kidney disease (p=0.003) was statistically significant between the hypertensive and resistant hypertension patients. Only 3% patients (n=4) have high medical adherence. CONCLUSION: Resistant hypertension is a prevalent clinical condition, and its prevalence is on the rise (n=130, 65%). With our ageing (≥40 years), normal BMI population, lack of physical activity, personal habits (smoking,alcohol), noncompliance with recommended diets, lack of physical activity, and failure to take antihypertensive medication on a regular basis, the associated increasing incidence of diabetes, hyperlipidemia, coronary artery disease and chronic kidney disease, resistant hypertension will be even more prevalent. [ABSTRACT FROM AUTHOR]