Principal Component Analysis with SVM for Disease Diagnosi
Juby Mathew1, R Vijayakumar2, Julie John3

1Dr.Juby Mathew, School of Computer Sciences, Mahatma Gandhi University, Kottayam, Kerala, India.
2Dr.R Vijayakumar, Professor, School of Computer Sciences, Mahatma Gandhi University, Kottayam, Kerala, India.
3Ms.Julie John, De Paul Institute of Technology, Angamaly.

Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 615-620 | Volume-8 Issue-8, June 2019 | Retrieval Number: H6762068819/19©BEIESP
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Abstract: Big data is the collection and analysis of a large set of data which holds many intelligence and raw information based on user data, Sensor data, Medical and Enterprise data. Since the volume of the medical data is increasing due to the presence of a vast number of features; the conventional rule mining technique is not competent to handle the data and to perform precise diagnosis. For instance, this paper intends to implement the improved rule mining technique to overcome the above-mentioned limitations. The model comes out with two main contribution stages (i) Using Map Reducing Framework (ii) Classification. Initially, the input medical data is given to map reduce framework. Here, Multi-linear Principle Component Analysis (MPCA) is used for reducing the given bulk data. Then, the reduced data is given to the classification process, where it classifies the disease with high accuracy. For this, this paper uses Support Vector Machine (SVM) classifier. After the completion of implementation, the proposed model compares its performance over other conventional methods like Principle Component Analysis- NN (PCA-NN), Independent Component Analysis- NN (ICA-NN) and MPCA-NN respectively in terms of performance measures like accuracy, specificity and, sensitivity, and the superiority of the proposed model is proven over other methods.
Keyword: Medical Data, Disease Diagnosis, Feature Extraction, NN Classification, MPCA.
Scope of the Article: Seismic Evaluation of Building Nonstructural Components.