Abstract
The problem of high dimensional clustering and classification has been well studied in previous articles. Also, the recommendation generation towards the treatment based on input symptoms has been considered in this research part. Number of approaches has been discussed earlier in literature towards disease prediction and recommendation generation. Still, the efficient of such recommendation systems are not up to noticeable rate. To improve the performance, an efficient multi level symptom similarity based disease prediction and recommendation generation has been presented. The method reads the input data set, performs preprocessing to remove the noisy records. In the second stage, the method performs Class Level Feature Similarity Clustering. The classification of input symptom set has been performed using MLSS (Multi Level Symptom Similarity) measure estimated between different class of samples. According to the selected class, the method selects higher frequent medicine set as recommendation using drug success rate and frequency measures. The proposed method improves the performance of clustering, disease prediction with higher efficient medicine recommendation.
Highlights
The modern society suffers with various diseases which has been increasing in every day in numbers
To improve the performance in clustering and other issues, this paper perform clustering according to class level feature similarity measure which has been measured based on the similarity in each dimension of data points
The measure would be used in disease prediction as well as increase the performance in recommendation system
Summary
The modern society suffers with various diseases which has been increasing in every day in numbers. The most algorithms consider only few number of features in clustering the data points of medical data set. This would reduce the performance of clustering and affect the performance of recommendation system. To perform clustering or map reduce, it is necessary to consider the maximum number of features in estimating similarity between the data points of cluster and input sample. The disease prediction is the process of predicting the possible disease would occur or based on the symptoms, identifying the possible disease It can be performed in many ways when the dimension of the data point is less. There will be similar symptoms for different diseases this increases the challenge in identifying the possible disease This increases the requirement of strategically approach in disease prediction.
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