International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 04 | Apr -2017
www.irjet.net
e-ISSN: 2395 -0056 p-ISSN: 2395-0072
An Adaptive Doctor Recommendation System using Data Mining Techniques Arvind D R1, D Ravindra1, Indresh H K1 , Mithun K B1 1Student,
Dept. of Computer Science and Engineering, The National Institute of Engineering, Mysuru, Karnataka, India
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Abstract – To find the specialty-counterpart, diagnosis-accurate, skill-superb and cost-effective doctors is not an easy job for the patients. In this paper, we describe a recommender framework to find the best doctors in accordance with patients’ requirements. In the proposed system, first it considers only those doctors whose profile match with patients’ requirements. Second, the best doctors will be recommended out of previously obtained doctors based on parameters such as patients’ feedbacks, education qualification and cost. Our paper will suggest a doctor recommendation system that uses data mining techniques, which can be used in those countries that have huge uneven distribution of medical resources.
Fig -1: Patients’ struggles to make decision without sufficient medical knowledge and experiences
Key Words: doctors, recommendation system, sentiment mining, relevance module, quality module.
In this paper, we propose a recommendation system that consists of the two main parts, one is relevance model and other is quality model. The quality model is based on data mining techniques such as clustering, sentiment – mining. Clustering is used to group the best doctors out of the given list of doctors. Sentiment mining will be performed on the comments given by the patients’ in the feedback form.
1. INTRODUCTION The massive amount of digital information is present on the internet. This information is unevenly distributed. So there is need for intelligent recommender system more than ever before. Recommender system is the one that filters vital information out of massive unevenly distributed information according to users’ preferences and interests. Lack of knowledge and relevant experiences make the people difficult in taking decisions regarding appropriate doctors for their treatments. In such situations, people make decisions based on other people recommendation, internet and advertisements. This causes unnecessary waste of money and time. People end up unsatisfied as shown in the Fig. 1.
The remainder of the paper is organized as follows. Section 2 provides various related research that deal with expert finding problems. Section 3 develops doctor recommendation system, consisting of relevance model and quality model. Section 4 demonstrates the final product in a computer for user to operate our recommendation system. This paper concludes in Section 5.
2. RELATED RESEARCH Hongxun Jiang and Wei Xn proposed an integrated doctor recommendation system that incorporates the relevance module, quality module based on web-mining and also provided the information regarding the drawbacks of the traditional procedures which was followed by the patients to find the appropriate patients. Relevance module is used to compute relevance between patients’ requirements and doctor profile. Quality module is used to analyze the best doctor qualities [1]. Ashish Jha, the physician provides top ten qualities through which the best doctors can be measured. He also proves that only education qualification shouldn’t be considered as a criterion for the prediction. He focused mainly qualities
Our recommendation system will overcome the problems faced by people in finding the appropriate doctors. This paper will provide the recommendation system that suggests the best doctors using data mining techniques. The best doctors are the one who understand the patients’ problems, care for them, respect them regardless of who they are and treat the diseases properly. Our system will suggest the best doctors by considering the facts such as patients’ feedbacks about the doctors, education qualifications, availability and cost.
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