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A Study on Outlier Detection and Feature Engineering Strategies in Machine Learning for Heart Disease Prediction

Computer Systems Science & Engineering 2024年第5期48卷 1085-1112页

作者： Varada Rajkumar Kukkala Surapaneni Phani Praveen Naga Satya Koti Mani Kumar Tirumanadham Parvathaneni Naga Srinivasu department of Computer Science&Engineering(aiml) MLR Institute of TechnologyHyderabad500043India department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyVijayawada520007India department of Computer Science&Engineering Sir C.R.Reddy College of EngineeringEluru534001India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravati522503India department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil

This paper investigates the application ofmachine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely;Z-Score incorporated with GreyWolf Optimization(GWO)as well as Interquartile Range(IQR)coupled with Ant Colony Optimization(ACO).Using a performance index,it is shown that when compared with the Z-Score and GWO with AdaBoost,the IQR and ACO,with AdaBoost are not very accurate(89.0%vs.86.0%)and less discriminative(Area Under the Curve(AUC)score of 93.0%vs.91.0%).The Z-Score and GWO methods also outperformed the others in terms of precision,scoring 89.0%;and the recall was also found to be satisfactory,scoring 90.0%.Thus,the paper helps to reveal various specific benefits and drawbacks associated with different outlier detection and feature selection techniques,which can be important to consider in further improving various aspects of diagnostics in cardiovascular ***,these findings can enhance the knowledge of heart disease prediction and patient treatment using enhanced and innovativemachine learning(ML)*** findings when combined improve patient therapy knowledge and cardiac disease prediction through the use of cutting-edge and improved machine learning *** work lays the groundwork for more precise diagnosis models by highlighting the benefits of combining multiple optimization *** studies should focus on maximizing patient outcomes and model efficacy through research on these combinations.

关键词： Grey wolf optimization ant colony optimization Z-Score interquartile range(IQR) AdaBoost outlier

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Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

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Computer Systems Science & Engineering 2023年第4期45卷 869-886页

作者： Sirisati Ranga Swamy SPhani Praveen Shakeel Ahmed Parvathaneni Naga Srinivasu Abdulaziz Alhumam department of CSE Vignan’s Institute of Management and Technology for Women GhatkesarTelanganaIndia department of CSE PVPSITVijayawadaAndhra Pradesh520007India department of Computer Science College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia department of Computer Science and Engineering-aiml VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India

Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at *** pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already *** established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the *** problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical *** models help predict the effects of the disease outbreak and help detect the effects in the coming *** this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan *** are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from *** current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better *** model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion *** is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

关键词： Chest CT COVID-19 classification ROC curves multi-feature disease analysis

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Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

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Computer Systems Science & Engineering 2023年第10期47卷 375-391页

作者： Sultan Alahmari Saud Yonbawi Suneetha Racharla ELaxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa King Abdulaziz City for Science and Technology P.O Box 6086Riyadh 11442Saudi Arabia department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia department of aiml Aditya Engineering CollegeSurempallemAndhra PradeshIndia department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed *** spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine *** accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural *** is significant for the prediction and growth monitoring of crop *** the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature *** article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm *** proposed HMAODL-CTC model mainly intends to categorize different types of crops on *** accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image *** addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature *** hyperparameter tuning of the dilated CNN model,the HMAO algorithm is ***,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC *** comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.

关键词： Crop type classification hyperspectral images agricultural monitoring deep learning metaheuristics

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Effect of a nitric oxide synthase inhibitor NG-nitro-L-arginine methyl ester on invasion of human colorectal cancer cell line SL-174T

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World Journal of Gastroenterology 2005年第40期11卷 6385-6388页

作者： Li-Bo YU Xin-Shu Dong Wen-Zhou sun Dong-Lu Zhao Yue Yang department of GeneralSurgery the Affiliated Tumor Hospital of Harbin MedicalUniversity Harbin 150040 Heilongiiang Province China Cancer Research Institute of HarbinMedical University Harbin 150040 Heilongjiang Province China

aiml To investigate the effect and mechanism of action of the nitric oxide synthase （NOS） inhibitor NG-nitro-L-arginine methyl ester （L-NAME） on invasion and metastasis of human colorectal cancer cell line SL-174T. METHODS： Human colorectal cancer cel4 line SL-174T was cultured and treated separately with four different dosages of L-NAME for 72 h, Nitric oxide （NO） production was measured with Griess reagent, The effect of L-NAME on invasion and migration of SL-174T cells were evaluated by using Transwell chambers attached with polycarbonate filters and reconstituted basement membrane （Matrigel）, RT-PCR was performed to determine the mRNA levels of matrix metalloproteinase-2 （MMP-2） and tissue inhibitor metalloproteinase-2 （TIMP-2）,RESULTS： L-NAME could significantly inhibit NO production of SL-174T in a dose-dependent manner. After being treated for 72 h with 0.2, 0.4, 0.8, and 1.0 mmol/L L- NAME, respectively, the ability of the L-NAME treated SL- 174T cells to invade the reconstituted basement membrane decreased significantly （t = 8.056, P〈0.05; t= 14.467, P〈0.01; t= 27.785, P〈0.01; and t= 29.405, P〈0.01, respectively） and the inhibition rates were 10.29%, 19.62%, 34.08%, and 42.23%, respectively. Moreover, L-NAME could inhibit migration of SL-174T cells, and the inhibition rates were 20.76%, 24.95%, 39.43%, and 46. 85% for L-NAME at 0.2, 0.4, 0.8, and 1.0 mmol/L, respectively （t = 15.116, P〈0.01）. In addition, after treatment with L-NAME, expression of MMP-2 mRNA was significantly decreased （t = 71.238, P〈0.01） and that of TIMP-2 mRNA was markedly increased （t = -13.020, P〈0.01）. CONCLUSION： L-NAME exerts anti-invasive and anti- metastatic effects on SL-174T cell line via downregulating MNP-2 mRNA expression and upregulating TIMP-2 mRNA expression.

关键词： Colorectal Neoplasms Neoplasm Invasiveness Cell Line, Tumor Cell Movement Dose-Response Relationship, Drug Female Gelatinase A Humans Male NG-Nitroarginine Methyl Ester Nitric Oxide Nitric Oxide Synthase inhibitors Tissue Inhibitor of Metalloproteinase-2

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