Diabetes Prediction in Healthcare at Early Stage Using Machine Learning Approach

Crop Diseases

Qualitative and quantitative agricultural production leads to economic benefits which can be achieved by periodic monitoring of crop, detection and prevention of crop diseases and insects. Quality of crop production is reduced by pest infection and crop diseases. Existing measures involves manual detection of cotton diseases by farmers and experts which requires regular monitoring and detection manifest at middle to later stage of infection which causes many disadvantages such as becoming too late for diseases to be cured. Lack of early detection of diseases causes the diseases to be spread in nearby crops in the field and also spraying of pesticides is done on entire field for minimizing the infection of disease. The main goal of proposed research topic is to find the solution to the agriculture problem which involves detecting disease in cotton plant at early stage and classify the disease based on symptoms. Early detection of disease at an early stage prevent it from spreading to another area and preventive measures can be taken by farmers by spraying pesticides to control its growth which helps to increase the cotton yield production. Automatic identification of the different diseases affecting cotton crop will give many benefits to the farmers so that time, money will be saved and also gives healthy life to the crop. The contribution of this paper is to present the machine learning approach used for cotton crop disease diagnosis and classification.

A Comparative Analysis of Early Stage Diabetes Prediction using Machine Learning and Deep Learning Approach

10.1109/ispcc53510.2021.9609364 ◽

2021 ◽

Author(s):

Md Abu Rumman Refat ◽

Md. Al Amin ◽

Chetna Kaushal ◽

Mst Nilufa Yeasmin ◽

Md Khairul Islam

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Comparative Analysis ◽

Early Stage ◽

Learning Approach ◽

Diabetes Prediction

An Innovative Machine Learning Approach to Diagnose Cancer at an Early Stage

Data Analytics in Bioinformatics ◽

10.1002/9781119785620.ch13 ◽

2021 ◽

pp. 313-337

Author(s):

P. Poongodi ◽

E. Udayakumar ◽

K. Srihari ◽

Nandan Mohanty Sachi

Keyword(s):

Machine Learning ◽

Early Stage ◽

Learning Approach ◽

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

10.1101/478065 ◽

2018 ◽

Cited By ~ 2

Author(s):

Nathan Wan ◽

David Weinberg ◽

Tzu-Yu Liu ◽

Katherine Niehaus ◽

Daniel Delubac ◽

...

Keyword(s):

Colorectal Cancer ◽

Machine Learning ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Cross Validation ◽

Early Stage ◽

Learning Approach ◽

Whole Genome ◽

Free Dna ◽

AbstractBackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.MethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.ResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.ConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

Machine Learning Approach For Diabetes Prediction

International Journal of Information Systems and Computer Sciences ◽

10.30534/ijiscs/2019/13822019 ◽

2019 ◽

Vol 8 (2) ◽

pp. 55-58 ◽

Cited By ~ 1

Author(s):

Jensia Thomas ◽

◽

Anumol Joseph ◽

Irene Johnson ◽

Jeena Thomas ◽

...

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Diabetes Prediction

Early Predictive Analytics in Healthcare for Diabetes Prediction Using Machine Learning Approach

10.1109/icccnt51525.2021.9579799 ◽

2021 ◽

Author(s):

Md. Mehedi Hassan ◽

Md. Al Mamun Billah ◽

Md. Mushfiqur Rahman ◽

Sadika Zaman ◽

Md. Mehadi Hasan Shakil ◽

...

Keyword(s):

Machine Learning ◽

Predictive Analytics ◽

Learning Approach ◽

Diabetes Prediction

Fast FMCW Terahertz Imaging for In-Process Defect Detection in Press Sleeves for the Paper Industry and Image Evaluation with a Machine Learning Approach

Sensors ◽

10.3390/s21196569 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6569

Author(s):

Maris Bauer ◽

Raphael Hussung ◽

Carsten Matheis ◽

Hermann Reichert ◽

Peter Weichenberger ◽

...

Keyword(s):

Machine Learning ◽

Continuous Wave ◽

Imaging System ◽

Early Stage ◽

Paper Industry ◽

Terahertz Imaging ◽

Image Resolution ◽

Structural Defects ◽

Learning Approach ◽

We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed in several milling steps afterwards. Due to a stabilizing polyester fiber mesh inlay, small defects can form on the sleeve’s backside already during the initial molding, however, they cannot be visually inspected until the whole production processes is completed. We have developed a fast-scanning frequenc-modulated continuous wave (FMCW) terahertz imaging system, which can be integrated into the manufacturing process to yield high resolution images of the press sleeves and therefore can help to visualize hidden structural defects at an early stage of fabrication. This can save valuable time and resources during the production process. Our terahertz system can record images at 0.3 and 0.5 THz and we achieve data acquisition rates of at least 20 kHz, exploiting the fast rotational speed of the barrels during production to yield sub-millimeter image resolution. The potential of automated defect recognition by a simple machine learning approach for anomaly detection is also demonstrated and discussed.

Predicting the epidemic trend of COVID-19 in China and across the world using the machine learning approach

10.1101/2020.03.18.20038117 ◽

2020 ◽

Cited By ~ 2

Author(s):

Mengyuan Li ◽

Zhilan Zhang ◽

Shanmei Jiang ◽

Qian Liu ◽

Canping Chen ◽

...

Keyword(s):

Machine Learning ◽

Early Stage ◽

Control Measures ◽

Mitigation Measures ◽

Learning Approach ◽

Foreseeable Future ◽

Stringent Control ◽

The World ◽

Death Cases

AbstractBackgroundAlthough COVID-19 has been well controlled in China, it is rapidly spreading outside the country and may have catastrophic results globally without implementation of necessary mitigation measures. Because the COVID-19 outbreak has made comprehensive and profound impacts on the world, an accurate prediction of its epidemic trend is significant. Although many studies have predicted the COVID-19 epidemic trend, most have used early-stage data and focused on Chinese cases.MethodsWe first built models to predict daily numbers of cumulative confirmed cases (CCCs), new cases (NCs), and death cases (DCs) of COVID-19 in China based on data from January 20, 2020, to March 1, 2020. Based on these models, we built models to predict the epidemic trend across the world (outside China). We also built models to predict the epidemic trend in Italy, Spain, Germany, France, UK, and USA where COVID-19 is rapidly spreading.ResultsThe COVID-19 outbreak will have peaked on February 22, 2020, in China and will peak on May 22, 2020, across the world. It will be basically under control in early April 2020 in China and late August 2020 across the world. The total number of COVID-19 cases will reach around 89,000 in China and 6,126,000 across the world during the epidemic. Around 4,000 and 290,000 people will die of COVID-19 in China and across the world, respectively. The COVID-19 outbreak will have peaked recently in Italy and will peak in Spain, Germany, France, UK, and USA within two weeks.ConclusionThe COVID-19 outbreak is controllable in the foreseeable future if comprehensive and stringent control measures are taken.

Estimate of the HOMA-IR Cut-off Value Identifying Subjects at Risk of Insulin Resistance using a Machine Learning Approach

Sultan Qaboos University Medical Journal [SQUMJ] ◽

10.18295/squmj.4.2021.030 ◽

2021 ◽

Author(s):

Abdelhamid Abdessalem ◽

Hamza Zidoum ◽

Fahd Zadjali ◽

Rachid Hedjam ◽

Aliya Al-Ansari ◽

...

Keyword(s):

Machine Learning ◽

Insulin Resistance ◽

At Risk ◽

Clinical Data ◽

Clustering Algorithm ◽

Early Stage ◽

Strong Relationship ◽

Learning Approach ◽

Research Findings

Objective: This paper describes an unsupervised Machine Learning approach to estimate the HOMA-IR cut-off identifying subjects at risk of insulin resistance in a given ethnic group, based on the clinical data of a representative sample. Methods: We apply the approach to clinical data of individuals of Arab ancestors obtained from a family study conducted in the city of Nizwa between January 2000 and December 2004. First, we identify HOMA-IR-correlated variables to which we apply our own clustering algorithm. Two clusters having the smallest overlap in their HOMA-IR values are returned. These clusters represent samples of two populations: insulin sensitive subjects and individuals at risk of insulin resistance. The cut-off value is estimated from intersections of the Gaussian functions modelling the HOMA-IR distributions of these populations. Results: We identified a HOMA-IR cut-off value of 1.62+/-0.06. We demonstrated the validity of this cut-off by 1) Showing that clinical characteristics of the identified groups match well published research findings about insulin resistance. 2) Showing a strong relationship between the segmentations resulting from the proposed cut-off and that resulting from the 2-hours glucose cut-off recommended by WHO for detecting prediabetes. Finally, we showed that the method is also able to identify cut-off values for similar problems (e.g. fasting sugar cut-off for prediabetes). Conclusion: The proposed method defines a HOMA-IR cut-off value for detecting individuals at risk of insulin resistance. Such method can identify high risk individuals at early stage which may prevent or at least delay the onset of chronic diseases like type 2 diabetes. Keywords: Machine Learning; Feature Selection; K-mean++ Clustering; Insulin Resistance; HOMA-IR; T2DM.

Machine Learning Reveals the Unique Biomarkers of Clonal Hematopoiesis in Patient With Early-Stage Colorectal Neoplasia: A Case Control Study

10.21203/rs.3.rs-431856/v1 ◽

2021 ◽

Author(s):

Yin-Chen Hsu ◽

Sin-Ming Huang ◽

Li-Chun Chang ◽

Yan-Ming Chen ◽

Wei-Tzu Chiu ◽

...

Keyword(s):

Machine Learning ◽

Blood Test ◽

Early Stage ◽

Colorectal Neoplasia ◽

Learning Approach ◽

Signature Analysis ◽

Clonal Hematopoiesis ◽

Mutational Signature ◽

Machine Learning Model ◽

Abstract BACKGROUND: Blood test has a better uptake for colorectal cancer screening than stool test and colonoscopy but suboptimal detection of early-stage colorectal neoplasia (CRN), including advanced adenoma and stage I cancer, limits its application. The present study aimed to evaluate whether clonal hematopoiesis (CH) from peripheral blood can be used as a biomarker for early-stage CRN screening and improve the detection of blood tests by machine-learning approach.METHODS: The CH profile was evaluated in 63 early-stage CRNs and 32 controls by error-corrected sequencing and classified by machine-learning method. Diagnostic performance was measured by receiver operator characteristic analysis. Additional 20 early-stage CRNs and 10 controls were used to validate the machine-learning model. We simultaneously used mutational signature analysis to study predictors based on CH.RESULTS: We identified 1,446 variants and clarified the uniqueness of variants from the peripheral bloods of early-stage CRNs. The machine learning model identified early-stage CRNs from controls and its AUC, sensitivity and specificity were 0.988, 94.2% and 99.3%, respectively. The CH-based CRN detection model was further verified. The accuracy, sensitivity, and specificity were 0.933 (p=0.00065), 95.0%, and 90.0%, respectively. Furthermore, the mutational signature analysis of those unique variants in CRNs revealed the influence of genetic architecture on DNA damages.CONCLUSIONS: Our results reveal the potential of CH to a mark produced by the carcinogenesis in early-stage CRN. We developed a CH-based blood test with machine learning approach, which not only increase screening uptake but also improve the detection rate of early-stage CRN.