Saurav Mallik

Machine learning screening for polycystic ovary syndrome has long reported a single binary accuracy, hiding differences between clinical subtypes. On a public dataset of 541 patients, a tuned random forest reached per-subtype accuracy ranging from 75.0% on the rarest subtype to 97.8% on the most common, a 22.8-point spread that stayed at 23.3 points after removing the seven variables used to define subtypes. A multi-task neural network with random-forest feature ranking and genetic-algorithm tuning predicts the binary label and the subtype together, holding 90.2% binary accuracy while lifting rarest-subtype recall from 75.0% to 87.5% and unclassified-case recall from the multi-class model's 31.5% to 79.6%.

Machine learning screening for polycystic ovary syndrome has long reported a single binary accuracy, hiding differences between clinical subtypes. On a public dataset of 541 patients, a tuned random forest reached per-subtype accuracy ranging from 75.0% on the rarest subtype to 97.8% on the most common, a 22.8-point spread that stayed at 23.3 points after removing the seven variables used to define subtypes. A multi-task neural network with random-forest feature ranking and genetic-algorithm tuning predicts the binary label and the subtype together, holding 90.2% binary accuracy while lifting rarest-subtype recall from 75.0% to 87.5% and unclassified-case recall from the multi-class model's 31.5% to 79.6%.