International Journals

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    PERFORMANCE ANALYSIS OF PREDICTING SURVIVAL RATES IN IMBALANCED HEALTHCARE DATASET.
    (International Journal of Advanced Research in Computer Science, 2018-04) R, Vani
    Predicting Patients health is a critical task in the Healthcare Industry. Healthcare datasets show a high degree of imbalance especially for rare diseases. The current work aims at predicting the post operative survival rate in thoracic surgery datasets. The dataset exhibits data imbalance with around 15% positive cases and remaining 85% negative cases. The commonly applicable machine learning techniques for prediction score poorly in predicting the positive cases in spite of high accuracy of the predictions for the negative cases. We use SMOTE (synthetic minority oversampling technique) to reduce the degree of imbalance and increase the positive samples proportion before the application of the following classifiers: Naive Bayes, Neural Networks, Random Forest, Boosting algorithms - Adaboost, Extreme Gradient boosting and Support Vector Machines and examine the results. The study shows that SVM and Naïve Bayes show significantly better performance on the imbalanced datasets than other models using synthetic datasets than under normal conditions.
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    ACUTE CYSTITIS AND ACUTE NEPHRITIS PREDICTION USING MACHINE LEARNING TECHNIQUES
    (Global Journal of Computer Science and Technology, 2010-09) Kowsalya R; Sasikala G; Sangeetha Priya J
    Urinary System includes kidneys, bladder, ureters and urethra. This is the major system involves electrolyte balance of the body and filters the blood and excretes the waste products in the form urine. Even the small disturbance in the renal function will step in a disasters manifestation. Among them we are considering the two diseases that affect the system are acute cystitis and acute nephritis. This paper presents the implementation of three supervised learning algorithms, ZeroR, J48 and Naive Bayes in WEKA environment. The classification models were trained using the data collected from 120 patients. The trained models were then used for predicting the acute cystitis or acute nephritis of the patients. The prediction accuracy of the classifiers was evaluated using 10-fold cross validation and the results were compared.