A neural network based clinical decision-support system for efficient diagnosis and fuzzy-based prescription of gynecological diseases using homoeopathic medicinal system

  • Authors:

  • Ashish Mangalampalli;Srirama Moorthy Mangalampalli;Rama Chakravarthy;Ajeet K. Jain

  • Affiliations:

  • Computer Science and Engineering Department, ICFAI Institute of Science and Technology, ICFAI University, 4-102, Jeedimetla, Medchal Road, Hyderabad 500 055, India;S S Consultants, Hyderabad, India;Biosciences Department, ICFAI Institute of Science and Technology, ICFAI University, Hyderabad, India;Computer Science and Engineering Department, ICFAI Institute of Science and Technology, ICFAI University, Hyderabad, India

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2006

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Abstract

As the analysis and diagnosis of gynecological diseases, especially using the homoeopathic system of medicine, gets more and more complicated, it becomes important for us to develop a decision-support system which can help a gynecologist analyze and prescribe medicines for such diseases with fewer errors. The prescription of a medicine for a gynecological disease, according to the homoeopathic system of medicine, is based on various modalities or symptoms of the disease. A medicine cannot be prescribed just by knowing the disease as is done in the case of the allopathic system of medicine or the general system of medicine. It becomes very difficult to take into account so many modalities while prescribing a medicine. A clinical decision-support system for gynecological diseases using the homoeopathic system of medicine for comprehensive diagnosis is extremely rare to find. The aim of this project is to develop a decision-support system, which will suggest a medicine for a gynecological disease based on the primary and secondary symptoms of the disease. The knowledge base of experts in this field has been utilized to develop this decision-support system. The decision-support system is based on the neural network concept. The rules for the decision-making are generated by combining a few neurons. The multi-layered weight-oriented feed forward neural network structure of the decision-support system makes it more robust, error-free and easy to program. This decision-support system, developed using Turbo prolog, will help a gynecologist to select the medicine of a particular gynecological disease quickly, easily and precisely. The primary symptoms help to evaluate a tentative medicine and the secondary symptoms confirm this tentative medicine. Fuzzy-type decision-making is used while analyzing the various symptoms.