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@article{159182,
        author = {M. Prakash and E.Loganathan and G.Sivakumar},
        title = {Drug component analysis based on Fuzzified Decision tree optimized neural network for disease related drug  recommendation},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {11},
        pages = {924-929},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159182},
        abstract = {Drug component analysis is an important factor for recommending disease oriented treatment for handling patients. Due to improper combination drugs prescription in medical industries cause more side effect by taking the medicinal drugs. So the combination of related feature analysis is main thing for identifying the correct the combinational facts. Most of the previous methodologies doesn’t carry to find drug relation to make combination. To resolve this problem, we propose a Drug feature penetration similarity rate (DFPSR)and deep Fuzzified Decision tree optimized neural network (FDTONN) classifier is applied to find the relational drug combination factor for recommending correct drug to the patients. The selected features are trained with FDTONN. This selected the drug pattern relation based on disease prone weight recommending the best features. The proposed system achieves high performance compared to the other existing system to solving the feature dimensionality ratio to make higher precision, recall rate, f-measure in redundant time complexity. This produce best performance as well other methods to attain best recommendation to the patients.},
        keywords = {drug data analysis: ANN, Fuzzy, feature selection and classification, machine learning.},
        month = {},
        }