Abstract: In recent times, an exponential utilization of information resources and advancements in data analytic tools results in the extended use of big data. Security and privacy are considered as the major issues that existed in the cloud based big data platform, particularly in the healthcare sector. On the other hand, there is a requirement of efficient model for handling big data and has received attention among different researchers. This paper presents Encryption Enabled Metaheuristic Optimization-based Feed Forward Neural Network (EEMO-FFNN) for cloud based big data environment. The presented EEMOFFNN model intends to perform secure communication and effective big data analytics in the healthcare environment. The EEMO-FFNN model initially enables the augmentation of patient data using SMOTE for the generation of big data. Next, secure data transmission from the source to cloud server takes place using Elliptic Curve Cryptography (ECC) based encryption technique. Besides, the MO-FFNN model based data classification process is performed on the Hadoop ecosystem to identify the existence of the disease. In order to adjust the weight and bias parameters involved in the FFNN model, the Salp Swarm Optimization (SSA) algorithm is applied. An extensive set of simulations were performed to ensure the effectual outcome of the EEMO-FFNN model and the results are examined under distinct aspects.