Data Mining &amp; Artificial Intelligence Techniques for Prediction of Heart Disorders: A Survey

Powar, Amogh; Shilvant, Seema; Pawar, Varsha; Parab, Vishesh; Shetgaonkar, Pratiksha; Aswale, Shailendra

doi:10.1109/vitecon.2019.8899547

Cited by 8 publications

(1 citation statement)

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“…Diagnostic tools use predictive algorithms to infer the presence or severity of a particular disease. The availability of a wide variety of data has made it possible the application of these technologies to various diseases, such as cancer (Cardillo et al 2017;Huang et al 2019), neurodegenerative diseases (Coviello et al 2020;Diaz et al 2019;Lella et al 2019;Vessio 2019), heart disorders (Azar et al 2016;Powar et al 2019), diabetes (El-Sappagh et al 2018), sleep apnea (Mencar et al 2019), just to mention a few.…”

Section: Related Workmentioning

confidence: 99%

MicroRNA expression classification for pediatric multiple sclerosis identification

Casalino

Castellano

Consiglio

et al. 2021

J Ambient Intell Human Comput

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MicroRNAs (miRNAs) are a set of short non-coding RNAs that play significant regulatory roles in cells. The study of miRNA data produced by Next-Generation Sequencing techniques can be of valid help for the analysis of multifactorial diseases, such as Multiple Sclerosis (MS). Although extensive studies have been conducted on young adults affected by MS, very little work has been done to investigate the pathogenic mechanisms in pediatric patients, and none from a machine learning perspective. In this work, we report the experimental results of a classification study aimed at evaluating the effectiveness of machine learning methods in automatically distinguishing pediatric MS from healthy children, based on their miRNA expression profiles. Additionally, since Attention Deficit Hyperactivity Disorder (ADHD) shares some cognitive impairments with pediatric MS, we also included patients affected by ADHD in our study. Encouraging results were obtained with an artificial neural network model based on a set of features automatically selected by feature selection algorithms. The results obtained show that models developed on automatically selected features overcome models based on a set of features selected by human experts. Developing an automatic predictive model can support clinicians in early MS diagnosis and provide new insights that can help find novel molecular pathways involved in MS disease.

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