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    A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model
    (Pergamon-Elsevier Science Ltd, 2023) Tiwari, Shamik; Jain, Anurag; Sapra, Varun; Koundal, Deepika; Alenezi, Fayad; Polat, Kemal; Alhudhaif, Adi; Nour, Majid
    Automatic screening approaches can help diagnose Cardiovascular Disease (CVD) early, which is the leading source of mortality worldwide. Electrocardiogram (ECG/EKG)-based methods are frequently utilized to detect CVDs since they are a reliable and non-invasive tool. Due to this, Smart Cardiovascular Disease Detection System (SCDDS) has been offered in this manuscript to detect heart disease in advance. A wearable device embedded with electrodes and Internet of Things (IoT) sensors is utilized to record the EKG signals. Bluetooth is used to send EKG signals to the smartphone. The smartphone transfers the signals through an Android app to a pre-trained deep learning-based architecture deployed on the cloud. The architecture analyzes the EKG signal, and a heart report is communicated to the patient and advises further preventive action. We offered an ensembled Convolution Neural Network architecture (ConvNet) and Convolution Neural Network architecture - Long Short-Term Memory Networks (ConvNet-LSTM) architecture to detect atrial fibrillation heartbeats automatically. The architecture utilizes a convolutional neural network and long short-term memory network to extract local correlation features and capture the front-to-back dependencies of EKG sequence data. MIT-BIH atrial fibrillation database was utilized to design the architecture and achieved an overall categorization accuracy of 98% for the test set's heartbeat data. The findings of this work show that the suggested system has achieved significant accuracy with the ensembling of models. Such models can be deployed in wearable devices and smartphones for continuous monitoring and reporting of the heart.
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    SPOSDS: A smart polycystic ovary syndrome diagnostic system using machine learning
    (Pergamon-Elsevier Science Ltd, 2022) Tiwari, Shamik; Kane, Lalit; Koundal, Deepika; Jain, Anurag; Alhudhaif, Adi; Polat, Kemal
    Polycystic Ovary Syndrome (PCOS) is a hormonal disorder that affects a large percentage of women of reproductive age. PCOS causes imbalanced or delayed menstrual cycles and produces high levels of the male hormone. The ovaries may create a significant number of little fluid-filled sacs (follicles) yet fail to discharge eggs regularly. The actual cause of PCOS is uncertain. However, early exposure and curing, as well as weight loss, may lower the threat of long-term complications. This study focuses on PCOS diagnosis based on a clinical dataset supplied by Kottarathil, accessible via its Kaggle repository. Non-invasive screening parameters are used to evaluate a range of machine learning approaches for screening PCOS patients without the use of invasive diagnostics. According to the findings of the experiments, the Random Forest (RF) method outperforms the other prominent machine learning algorithms with an accuracy of 93.25%. Further, the out-of-bag (OOB) error is utilized for assessing the prediction performance of RF.