Impact of Patients’ Gender on Parkinson’s disease using Classification Algorithms

In this paper the accuracy of two machine learning algorithms including SVM and Bayesian Network are investigated as two important algorithms in diagnosis of Parkinson’s disease. We use Parkinson's disease data in the University of California, Irvine (UCI). In order to optimize the SVM algorithm, different kernel functions and C parameters have been used and our results show that SVM with C parameter (C-SVM) with average of 99.18% accuracy with Polynomial Kernel function in testing step, has better performance compared to the other Kernel functions such as RBF and Sigmoid as well as Bayesian Network algorithm. It is also shown that ten important factors in SVM algorithm are Jitter (Abs), Subject #, RPDE, PPE, Age, NHR, Shimmer APQ 11, NHR, Total-UPDRS, Shimmer (dB) and Shimmer. We also prove that the accuracy of our proposed C-SVM and RBF approaches is in direct proportion to the value of C parameter such that with increasing the amount of C, accuracy in both Kernel functions is increased. But unlike Polynomial and RBF, Sigmoid has an inverse relation with the amount of C. Indeed, by using these methods, we can find the most effective factors common in both genders (male and female). To the best of our knowledge there is no study on Parkinson's disease for identifying the most effective factors which are common in both genders

Imperfect distributed quantum phase estimation

In the near-term, the number of qubits in quantum computers will be limited to a few hundreds. Therefore, problems are often too large and complex to be run on quantum devices. By distributing quantum algorithms over different devices, larger problem instances can be run. This distributing however, often requires operations between two qubits of different devices. Using shared entangled states and classical communication, these operations between different devices can still be performed. In the ideal case of perfect fidelity, distributed quantum computing is a solution to achieving scalable quantum computers with a larger number of qubits. In this work we consider the effects on the output fidelity of a quantum algorithm when using noisy shared entangled states. We consider the quantum phase estimation algorithm and present two distribution schemes for the algorithm. We give the resource requirements for both and show that using less noisy shared entangled states results in a higher overall fidelity

Corrigendum to “Performance Analysis of Classification Algorithms on early detection of Liver disease” (Expert Systems with Applications (2017) 67 (239–251), (S095741741630464X) (10.1016/j.eswa.2016.08.065))

The authors regret to inform that there is a mistake in Table 6 according to this table, Table 7 Figure 7 should be changed. In the following we present the correct Tables and Figure. Table 6 in the paper had two mistakes that we corrected here. It should be noted that the bold and italic values show the changed values compared with previous results. Moreover, in page 249 and the first column (line 13 of the first paragraph) will be corrected according to the corrections presented in Table 7. Add the corrections/changes of the article here: Since, the places of both FN and FP were changed, therefore, related values in Table 7 and Figure 7 have been modified accordingly. The average in reports of the first paper was 96.552 and for the second paper that the decision tree has been used the best accuracy was 69.40 percent while according to Table 7, best specificity, sensitivity, precision and accuracy (for testing) in our study were 82.05, 97.52, 94.40 and 93.75 percent, respectively. The authors would like to apologize for any inconvenience caused

Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease

Background: Coronary artery disease (CAD) is one of the major and important causes of mortality worldwide. The knowledge about the risk factors which increases the probability of developing CAD can help to understand the disease better and also its treatment. Nowadays, many computer-aided approaches have been used for the prediction and diagnosis of diseases. The swarm intelligence algorithms like particle swarm optimization (PSO) have demonstrated great performance in solving different optimization problems. As rule discovery can be modeled as an optimization problem, it can be mapped to an optimization problem and solved by means of an evolutionary algorithm like PSO. Methods: An approach for discovering classification rules of CAD is proposed. The work is based on the real-world CAD dataset and aims at the detection of this disease by producing the accurate and effective rules. An approach based on a hybrid binary-real PSO algorithm is proposed which includes the combination of binary and realvalued encoding of a particle and a different approach for calculating the velocity of particles. The rules were developed from randomly generated particles which take random values in the range of each attribute in the rule. Two different feature selection approaches based on multi-objective evolutionary search and PSO were applied on the dataset and the most relevant features were selected by the algorithms. Results: The accuracy of two different rule sets were evaluated. The rule set with 11 features obtained more accurate results than the rule set with 13 features. Our results show that the proposed approach has the ability to produce effective rules with highest accuracy for the detection of CAD