HFIPO-DPNN: A Framework for Predicting the Dropout of Physically Impaired Student from Education

Education plays a significant role in individuals’ development and economic growth of the developing coun-tries like India. Dropout of students from their studies is the major concern for any order of education. Some models for predicting the dropout of students are developed with several factors. Many of them lacked consistencies as they backed their studies with the academic performance of the students. Especially, for those students suffered with physical impairment the drop out depends on several external factors. Students drop out of school for a variety of reasons, including financial difficulties, parents' unwillingness, distance and a lack of basic amenities, poor educational quality, an inadequate school environment and building, overcrowded classrooms, improper languages of instruction, carelessness on the part of teachers, and security issues in girls' schools. Hence, this work proposes a novel HFIPO-DPNN to predicting the physically handicapped student’s dropout from School also to predict the student dropout rooted on the previous semester marks. The proposed model enclosed the hybrid firefly and improved particle swarm algorithm to optimize the feature selection that influence the dropout of hearing-impaired students. The optimized feature data are used to predict the dropout with the novel DPNN. The optimized data was split and used for training the DPNN. The testing data is used to evaluate the performance of the proposed framework. The outcome for the proposed framework is evaluated on several metrics. The accuracy of the proposed model is about 99.02%. The HFIPO-DPNN framework can be enhanced for predicting the dropout for students with other disabilities. The optimization revealed that factors other than family factors should be taken into account when predicting dropout.