Medical Record Classification: A Modified Genetic Algorithm for Feature Selection

Medical record classification is the process of categorizing a patient’s record as either having or not having a medical condition based on some given information (features) about the patient. Not all available features about a patient are both useful and relevant in the process of classification. As such, the need for selecting the relevant and useful features arises. Furthermore, the current growth in data dimensionality as a result of falling cost of data capture and storage also makes it necessary to feed the learning algorithm with only the required features about the patient. Over the years, the ML Community has used a number of algorithms for feature selection. One of such widely used algorithms is Genetic Algorithm (GA). Given that the performance of GA is depended on algorithm parameters and genetic operators used, this work modified the genetic operators (crossover and mutation) of the GA and used Extreme Learning Machine (ELM) which is a Single Layer Feedforward Neural Network (SLFN) with faster training time and least parameter tuning for the purpose of record classification. Furthermore, the work evaluated the performance of the proposed algorithm on 3 datasets from the UCI ML repository. The proposed algorithm showed a faster convergence, better classifier accuracy and fewer selected features than the traditional GA and other reported works. The proposed method is particularly useful in situation of time constraint, low computation power and high dimensional data.