4. Conclusions and future work
In this study, an attempt has been made to develop a NNFL system for diagnosing depression based on seven different symptoms. Performance of NNFL-based diagnosing tool is improved through GA-based optimization. Both rule base as well as data base has been optimized using GA. Unfortunately, GA took huge time to evolve the good solution. There are reported works which have brought down the time consumed while running a GA-based optimizer in machine automation. However, it is important to note here that diagnosis of depression consumes months to get diagnosed even by an expert psychiatrist and therefore speed is out of the scope of the paper. Still, to reduce the time the paper attempts to identify the redundant rules and analysis has been made to remove them by identifying the load of individual rules on the morbidity. Finally, performance of the optimized GAtuned NNFL system is compared with that of back-propagation based NNFL system for ten different cases. It was found that the performance of GA-tuned NNFL system is better compared to the other one. Moreover, it has been observed that GA-tuned NNFL system has outperformed to a NN-based system. Therefore, Approach 2 can be of great help to the doctors for diagnosing the patients suffering from depression.
Presently forward mapping has been between the symptoms and the disease. However, it will be much more interesting if the reverse mapping is made, where it will be possible to identify the impact of a symptom on the disease. The authors are presently working with this problem. It is also worth noting while reviewing literature we have come across few works on the said problems.
