Every woman is at risk of ovarian cancer; about 90 percent of women who develop ovarian cancer are above 40 years of age, with the high number of ovarian cancers occurring at the age of 60 years and above. Early and correct diagnosis of ovarian cancer can allow proper treatment and as a result reduce the mortality rate. In this paper, we proposed a hybrid of Synthetic Minority Over-sampling Technique (SMOTE) and Artificial Neural Network (ANN) to diagnose ovarian cancer from public available ovarian dataset. The dataset were firstly preprocessed using SMOTE before employing Neural Network for classification. This study shows that performance of Neural Networks in the cancer classification is improved by employing SMOTE preprocessing algorithm to reduce the effect of data imbalance in the dataset. To justify the performance of the proposed approach, we compared our results with the standard neural Network algorithms. The performance measurement evaluated was based on the accuracy, F-measure, Recall, ROC Area Margin Curve and Precision. The results showed that SMOTE+MLP (with above 96% accuracy) performed better than SMOTE+RBF and standard RBF and MLP.

With the intricate interplay between clinical and pathological data in coronary heart disease (CHD) diagnosis, there is a growing interest among researchers and healthcare providers in developing more accurate and reliable predictive methods. In this paper, we propose a new method entitled the robust artificial neural Network classifier (RANNC) technique for the prediction of CHD. The dataset CHD in this paper has imbalanced data, and in addition, it has some outlier values. The dataset consists of information related to 4240 samples with 16 attributes. Due to the presence of outliers, a robust method has been used to scale the dataset. On the other hand, due to the imbalance of CHD data, three data balancing methods, including Random Over sampling (ROS), Synthetic Minority Over sampling Technique (SMOTE), and Adaptive Synthetic sampling (ADASYN) approaches, have been applied to the CHD data set. Also, six artificial intelligence algorithms, including LRC, DTC, RFC, KNNC, SVC, and ANN, have been evaluated on the considered dataset with criteria such as precision, accuracy, recall, F1-score, and MCC. The RANNC, leveraging ADASYN to address data imbalance and outliers, significantly improved CHD diagnostic accuracy and the reliability of healthcare predictive models. It outperformed other artificial intelligence methods, achieving precision, accuracy, recall, F1-score, and MCC scores of 95.57%, 96.90%, 99.70%, 97.59%, and 93.42%, respectively.

Background and Objectives: Subsampling methods allow sampling signals at rates much lower than Nyquist rate by using low-cost and low-power analog-to-digital converters (ADC). These methods are important for systems such as sensor Networks that the cost and power consumption of sensors are the core issue in them. The Chinese remainder theorem (CRT) reconstructs a large integer (input frequency) from its multiple remainders (aliased or under-sampled frequencies), which are produced from under-sampling or integer division by several smaller positive integers. sampling frequencies can be reduced by approaches based on CRT.Methods: The largest dynamic range of a generalized Chinese remainder theorem for two integers (input frequencies) has already been introduced in previous works. This is equivalent to determine the largest possible range of the frequencies for a sinusoidal waveform with two frequencies which the frequencies of the signal can be reconstructed uniquely by very low sampling frequencies. In this study, the largest dynamic range of CRT for any number of integers (any number of frequencies in a sinusoidal waveform) is proposed. It is also shown that the previous largest dynamic range for two frequencies in a waveform is a special case of our proposed procedure. Results: A procedure for multiple frequencies detection from reminders (under-sampled frequencies) is proposed and maximum tolerable noises of under-sampled frequencies for unique detection is obtained. The numerical examples show that the proposed approach, in some cases, can gain 11.5 times higher dynamic range than the conventional methods for a multi-sensor under-sampling system.Conclusion: Other studies introduced the largest dynamic range for the unique reconstruction of two frequencies by CRT. In this study, the largest dynamic ranges for any number of frequencies are investigated. Moreover, tolerable noise is also considered.