Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Algorithms

Bharati, Subrato; Podder, Prajoy; Mondal, M. Rubaiyat Hossain

doi:10.1109/tensymp50017.2020.9230932

Cited by 97 publications

(38 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, by examining the ultrasound the presence of follicles will be determined. Further, detecting the follicles by physicians through manual examination takes more time and formidable [15][16][17] because the ultrasound image contains speckle noise and artefacts. This is due to the presence of blood vessels, endometrium and tissues in the ovary that are also captured during the ultrasound scanning process.…”

Section: Srinivas Publicationmentioning

confidence: 99%

Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

Pulluparambil

Bhat

2021

IJHSP

View full text Add to dashboard Cite

Purpose: Considered as the most common hormonal disorder among women, polycystic ovary syndrome or PCOS affects 1 in 10 reproductive aged women (18 - 44 years). Ultrasonography is applied for assessing the ovaries to detect PCOS. The patients affected by PCOS consist of 10-12 cysts present in the ovary, but more than 10 cysts are more enough to diagnose the disorder from the ultrasound images. Then, by examining the ultrasound the presence of follicles will be determined. Therefore, the image processing approaches have assisted to identify the characteristics like follicle size, number of follicles and structure to minimize the workload and time of doctors. PCOS do not have better treatment and effective diagnosis. This paper includes reviewing a summary of some of the researches that have been going in area of medical diagnosis. Based on the review, research gap, research agendas to carry out further research are identified. Approach: A detailed study on the algorithms used in medical image processing and classification. Findings: The study indicated that most of the classification of polycystic ovarian syndrome is done merely on the clinical data sets. The new hybrid methodology proposed will be more precise as both images and lifestyle are analysed. Originality: The type of data required for detection system are studied and the architecture and schematic diagram of a proposed system are included. Paper Type: Literature Review.

show abstract

Section: Srinivas Publicationmentioning

confidence: 99%

Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

Pulluparambil

Bhat

2021

IJHSP

View full text Add to dashboard Cite

show abstract

“…Machine learning classifiers are successfully applied to classify normal (negative) cases and positive (having disease) cases for the case of many diseases [37][38][39][40][41][42][43][44][45][46][47][48][49][50]. Several classifiers such as RF [44], LR [43], SVM, XGBoost (XGB) are implemented in this work.…”

Section: Classification Algorithmsmentioning

confidence: 99%

“…These classifiers are often used in cancer disease prediction such as breast cancer, lung cancer, etc. [39,42], prediction of spinal abnormalities [37] and hepatitis disease prediction [38]. Therefore, these algorithms are applied to the dataset.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Forecasting the Spread of COVID-19 and ICU Requirements

et al. 2021

Self Cite

View full text Add to dashboard Cite

<span lang="EN-US">Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.</span>

show abstract

“…In the presence of the recent coronavirus disease 2019 (COVID-19) [62][63], [65][66], the importance of IoMT has greatly increased. Different machine learning [68][69] and deep learning techniques along with sensors [64], image processing [67] and wireless communication techniques Blockchain operations can also be utilized in efficient detection of the malware in IoT/IoMT environment. In such kind of detection method, we can create a block containing the information about the malicious programs (i.e., malware) to add in the blockchain.…”

Section: Future Scope and Research Directionmentioning

confidence: 99%

Review on the Security Threats of Internet of Things

Podder¹,

Mondal²,

Bharati³

et al. 2020

IJCA

Self Cite

View full text Add to dashboard Cite

Internet of Things (IoT) is being considered as the growth engine for industrial revolution 4.0. The combination of IoT, cloud computing and healthcare can contribute in ensuring well-being of people. One important challenge of IoT network is maintaining privacy and to overcome security threats. This paper provides a systematic review of the security aspects of IoT. Firstly, the application of IoT in industrial and medical service scenarios are described, and the security threats are discussed for the different layers of IoT healthcare architecture. Secondly, different types of existing malware including spyware, viruses, worms, keyloggers, and trojan horses are described in the context of IoT. Thirdly, some of the recent malware attacks such as Mirai, echobot and reaper are discussed. Next, a comparative discussion is presented on the effectiveness of different machine learning algorithms in mitigating the security threats. It is found that the k-nearest neighbor (kNN) machine learning algorithm exhibits excellent accuracy in detecting malware. This paper also reviews different tools for ransomware detection, classification and analysis. Finally, a discussion is presented on the existing security issues, open challenges and possible future scopes in ensuring IoT security.

show abstract

Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Algorithms

Cited by 97 publications

References 13 publications

Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

Forecasting the Spread of COVID-19 and ICU Requirements

Review on the Security Threats of Internet of Things

Contact Info

Product

Resources

About