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TAWSEEF SHAIKH

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Papers by TAWSEEF SHAIKH

Research paper thumbnail of A Prototype of Parkinson’s and Primary Tumor Diseases Prediction Using Data Mining Techniques

In the absence of medical diagnosis evidences, it is difficult for the experts to opine about the... more In the absence of medical diagnosis evidences, it is difficult for the experts to opine about the grade of disease with affirmation. Generally many tests are done that involve classification or clustering of large scale data. However , many tests can complicate the main diagnosis process and lead to the difficulty in obtaining end results, particularly in the case where many tests are performed. This could be solved by the aid of machine learning techniques. In this paper Psychiatric datasets of Parkinson’s & Primary tumor Diseases are modeled and used it to predict their probability in the patients. The performance of Artificial Neural Network , Decision Trees Algorithm and Naive Bayes Algorithm on these medical data were measured. The results showed that Artificial Neural Network performed best with accuracy of 90.7692 % ,then Decision trees with accuracy of 80.5128 % and finally NaiveBayes with accuracy 69.2308 % in case of Parkinson’s while as in primary tumor NavieBayes performs best with an accuracy of 49.1176%, then Artificial Neural Network with an accuracy of 42.0588% and lastly Decision trees with accuracy 32.3529%.

Research paper thumbnail of A Prototype of Parkinson’s and Primary Tumor Diseases Prediction Using Data Mining Techniques

In the absence of medical diagnosis evidences, it is difficult for the experts to opine about the... more In the absence of medical diagnosis evidences, it is difficult for the experts to opine about the grade of disease with affirmation. Generally many tests are done that involve classification or clustering of large scale data. However , many tests can complicate the main diagnosis process and lead to the difficulty in obtaining end results, particularly in the case where many tests are performed. This could be solved by the aid of machine learning techniques. In this paper Psychiatric datasets of Parkinson’s & Primary tumor Diseases are modeled and used it to predict their probability in the patients. The performance of Artificial Neural Network , Decision Trees Algorithm and Naive Bayes Algorithm on these medical data were measured. The results showed that Artificial Neural Network performed best with accuracy of 90.7692 % ,then Decision trees with accuracy of 80.5128 % and finally NaiveBayes with accuracy 69.2308 % in case of Parkinson’s while as in primary tumor NavieBayes performs best with an accuracy of 49.1176%, then Artificial Neural Network with an accuracy of 42.0588% and lastly Decision trees with accuracy 32.3529%.

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