Principal Component Analysis in the Local Differential Privacy Model

In this paper, we study the Principal Component Analysis (PCA) problem under the (distributed) non-interactive local differential privacy model. For the low dimensional case (i.e., p ≪ n ), we show the optimal rate of Θ ( k p n ϵ 2 ) (omitting the eigenvalue terms) for the private minimax risk of the k-dimensional PCA using the squared subspace distance as the measurement, where n is the sample size and ϵ is the privacy parameter. For the high dimensional (i.e., p ≫ n ) row sparse case, we first give a lower bound of Ω ( k s log ⁡ p n ϵ 2 ) on the private minimax risk, where s is the underlying sparsity parameter. Then we provide an efficient algorithm to achieve the upper bound of O ( s 2 log ⁡ p n ϵ 2 ) . Experiments on both synthetic and real world datasets confirm our theoretical guarantees.

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Principal Component Analysis in the Local Differential Privacy Model.

Risk factors for cardiovascular disease among undergraduate students in Edinburgh, Scotland

In this paper, we diagnose disease by implementing principal component analysis based on the user entered values for various symptoms. It considers components from the user entered values and compares them with the values in the database. Here the diagnosis is done by the method of prediction using trained data sets (TDS) and the results are compared by using suitable data matching systems (DMS).The TDS are provided by Intelligent System Laboratory of K.N. Toosi University of Technology, Imam Khomeini Hospital. If the user entered symptom values exist in the medical dataset then the percentage of getting an output that is true is 100%, if the symptom values do not exist, then the disease is predicted. PCA provides a precise disease prediction with the disease name and referral source which are optimized and neared to the true value in the datasets. Prediction based on machine learning algorithm gives an output that is 92%. This work predicts the actual levels of thyroid in human body. General Terms Hyperthyroid disease ,Prediction , Machine learning algorithms.

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Standard Cog Exploration on Medicinal Data

Intrauterine fetal hypoxia is one of the leading cause of perinatal mortality and morbidity. This can eventually lead to severe neurological damage like cerebral palsy and in extreme cases to fetal demise. It is thus necessary to monitor the fetus during intrapartum and antepartum period. Cardiotocograph (CTG) as a method of assessing the status of the fetus had been in use for last six decades. Nowadays it is the most widely used non-invasive technique for the continuous monitoring of the fetal heart rate (FHR) and the uterine contraction pressure (UCP). Though its introduction limited the birth related problems, the accuracy of interpretation was hindered by quite a few factors. Different guidelines that are provided for the interpretation are based on crisp logic which fails to capture the inherent uncertainty present in the medical diagnosis. Misinterpretations had led to inaccurate diagnosis which resulted in many medico-legal litigations. The vagueness present in the physician’s evaluation is best modeled using soft-computing based techniques. In this paper authors used the CTG dataset from UCI Irvine Machine Learning Data Repository which contains 2126 data and each data-point is represented by 37 features. Dimensionality of the feature set was reduced using different automated methods as well as manually by the physicians. The resulting data sets were classified using various machine learning algorithms. Aim of this study is to establish which set of features is best suited to give good insight into the status of the fetus and also determine the most effective machine learning technique for this purpose. The accuracy of the outcomes were measured using statistical methods such as sensitivity, specificity, precision, F-Measure, confusion matrix and kappa value. We obtained an accuracy of 99.91% and kappa measure of 0.997 when the feature set was reduced using MRMR.

R.C Smart

Papers

Principal component analysis in medical research