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Ovarian Cancer (OC) is a type of cancer that affects ovaries in women, and is difficult to detect at initial stage resulting to increased mortality rate. The OC data generated from the Internet of Medical Things (IoMT) can be used to identify distinguish the OC. To achieve this, we utilize Self Organizing Maps (SOM) and Optimal Recurrent Neural Networks (ORNN) to classify OC. SOM algorithm was utilized for better feature subset selection and was also utilized for separating profitable, understood and intriguing data from huge measures of medical data. In addition, an optimal classifier named optimal recurrent neural network (ORNN) is also employed. The classification rate of OC detection process can be improved by optimizing the weights of RNN structure using Adaptive Harmony Search Optimization (AHSO) algorithm. A set of experimentation is carried out using the data collected from women who have a high …