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Detecting-Malaria-using-Machine-Learning

Using clinical features to predict malaria diagnosis

  • This exercise use data dummy
  • The outcome variable is clinical diagnosis type: 'Non-malaria Infection', 'Severe Malaria', 'Uncomplicated Malaria'
  • Predictor will use blood examination includes:'wbc_count', 'rbc_count', 'hb_level', 'hematocrit', 'mean_cell_volume', 'mean_corp_hb', 'mean_cell_hb_conc', 'platelet_count', 'platelet_distr_width', 'mean_platelet_vl', 'neutrophils_percent', 'lymphocytes_percent', 'mixed_cells_percent', 'neutrophils_count', 'lymphocytes_count', 'mixed_cells_count', 'RBC_dist_width_Percent'
  • Data is analysed using Machine Learning, with supervised learning method and Regression

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