Autism Spectrum Disorder (ASD) Represents A Multifaceted Neuro-developmental State That Presents Significant Difficulties In Its Early Identification And Intervention. This Survey Explores The Recent Advancements And Methodologies In ASD Detection Leveraging Machine Learning (ML), Deep Learning (DL), And Neuroimaging Techniques. An Extensive Survey Of Literature Between 2018 And 2023 Reveals A Paradigm Shift In Diagnostic Approaches, Emphasizing The Integration Of ML Algorithms, Like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), And Decision-making Models, In Conjunction With Various Neuro-imaging Modalities Like Magnetic Resonance Imaging (MRI), Electroencephalography (EEG), And Functional Near-Infrared Spectroscopy (fNIRS). These Modalities Facilitate The Identification Of Distinctive Biomarkers, Behavioral Patterns, And Neural Correlates Associated With ASD. The Survey Also Looks At Potential Ethical Issues, The Importance Of Early Detection Using ML-driven Methodologies, And The Changing Diagnostic Tool Landscape That Aims To Offer Timely And Individualized Interventions For People With ASD. The Combination Of These Data Demonstrates The Revolutionary Effect Of ML, DL, And Neuro-imaging In Improving The Accuracy Of ASD Detection, Allowing Access To Additional Potent Intervention Methods And A More Thorough Understanding Of The Neurobiology Underlying The Condition.
Over The Years, Researchers Have Developed Several Expert Systems To Help Cardiologists Improve The Diagnostic Process By Predicting Heart Diseases Early On. Most Of The Available Machine Learning Approaches Are Complicated And They Were Generally Created For Use With Large Data. Unfortunately, These Approaches Can’t Be Effectively Used In The Scenarios With Small Data To Train The Model. In This View, This Paper Proposes A Simple And Effective Diagnostic System That Uses Extreme Gradient Boosting (XGBoost) With Feature Selection Algorithm To Predict Heart Disease In Case Of Dataset With Less Records. Proper Hyperparameter Tuning Is Vital For The Effective Deployment Of Any Classifier. To Improve The Hyperparameters Of XGBoost, Grid Search Is Employed, Which Is An Optimal Method For Hyperparameter Optimization. Also, The One-Hot (OH) Encoding Approach Is Employed To Encode Categorical Information In Cleveland Heart Disease Dataset. To Evaluate The Proposed Work, The Suggested Model Is Assessed And Compared To Other Classifiers. The Proposed Model Achieved An Area Under Curve (AUC) Of 0.853 And Prediction Accuracy Of 85.96%. From The Experimental Results, The Proposed Model Achieved Higher Accuracy When Compared To The Other Models.
The Paper Presents Development Of A Smart Infant Monitoring System Using Multiple Non-invasive Sensors To Detect Various Physiological Functions. The System Can Evaluate Different Physiological Activities Such As Respiration, Movement, Noise, Position, As Well As Ambient Temperature, And Humidity. By Processing The Acquired Data From Different Sensor Modules, The System Can Generate Alarm Signals For Adverse Situations Such As The Occurrence Of Apnea, Seizure, Or Noisy And Uncomfortable Environmental Conditions. The System Will Also Be Able To Detect Critical Respiratory Conditions By Analyzing Breathing Data And Saturated Blood Oxygen Level (SpO2) Using Machine Learning (ML) Models Such As Neural Networks. The Proposed System Allows The Caregiver To Monitor The Condition Of The Patient From A Remote Location By Implementing Wireless Communication With A Remote Computer Or A Cell Phone.
Accurate Diagnosis Of Parkinson's Disease (PD) At An Early Stage Is Challenging For Clinicians As Its Progression Is Very Slow. Currently Many Machine Learning And Deep Learning Approaches Are Used For Detection Of PD And They Are Popular Too. This Study Proposes Four Deep Learning Models And A Hybrid Model For The Early Detection Of PD. Further To Improve The Performance Of The Models, Grey Wolf Optimization (GWO) Is Used To Automatically Fine-tune The Hyperparameters Of The Models. The Simulation Study Is Carried Out Using Two Standard Datasets, T1,T2-weighted And SPECT DaTscan. The Metaherustic Enhanced Deep Learning Models Used Are GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 And GWO-VGG16 + InceptionV3. Simulation Results Demonstrated That All The Models Perform Well And Obtained Near Above 99% Of Accuracy. The AUC-ROC Score Of 99.99 Is Achieved By The GWO-VGG16 + InceptionV3 And GWO-DenseNet Models For T1, T2-weighted Dataset. Similarly, The GWO-DenseNet, GWO-InceptionV3 And GWO-VGG16 + InceptionV3 Models Result An AUC-ROC Score Of 100 For SPECT DaTscan Dataset.
Alzheimer's Disease (AD) Is A Progressive Neurological Disease Considered The Most Common Form Of Late-stage Dementia. Usually, AD Leads To A Reduction In Brain Volume, Impacting Various Functions. This Article Comprehensively Analyzes The AD Context In Fivefold Main Topic. Firstly, It Reviews The Main Imaging Techniques Used In Diagnosing AD Disease. Secondly, It Explores The Most Proposed Deep Learning (DL) Algorithms For Detecting The Disease. Thirdly, The Article Investigates The Commonly Used Datasets To Develop DL Techniques. Fourthly, We Conducted A Systematic Review And Selected 45 Papers Published In Highly Ranked Publishers (Science Direct, IEEE, Springer, And MDPI). We Analyzed Them Thoroughly By Delving Into The Stages Of AD Diagnosis And Emphasizing The Role Of Preprocessing Techniques. Lastly, The Paper Addresses The Remaining Practical Implications And Challenges In The AD Context. Building On The Analysis, This Survey Contributes To Covering Several Aspects Related To AD Disease That Have Not Been Studied Thoroughly.
Assessment And Outcome Monitoring Are Critical For The Effective Detection And Treatment Of Mental Illness. Traditional Methods Of Capturing Social, Functional, And Behavioral Data Are Limited To The Information That Patients Report Back To Their Health Care Provider At Selected Points In Time. As A Result, These Data Are Not Accurate Accounts Of Day-to-day Functioning, As They Are Often Influenced By Biases In Self-report. Mobile Technology (mobile Applications On Smartphones, Activity Bracelets) Has The Potential To Overcome Such Problems With Traditional Assessment And Provide Information About Patient Symptoms, Behavior, And Functioning In Real Time. Although The Use Of Sensors And Apps Are Widespread, Several Questions Remain In The Field Regarding The Reliability Of Off-the-shelf Apps And Sensors, Use Of These Tools By Consumers, And Provider Use Of These Data In Clinical Decision-making.
Online Recommender Systems Are Being Used Increasingly Often For Hospitals, Medical Professionals, And Drugs. Today, The Great Majority Of Consumers Look Online Before Asking Their Doctors For Prescription Suggestions For A Range Of Health Conditions. The Medical Suggestion System Can Be Valuable When Pandemics, Floods, Or Cyclones Hit. In The Age Of Machine Learning (ML), Recommender Systems Give More Accurate, Precise, And Reliable Clinical Predictions While Using Less Resources. The Medicine Recommendation System Gives The Patient Reliable Information About The Medication, The Dosage, And Any Possible Adverse Effects. Medication Is Given Based On The Patient's Symptoms, Blood Pressure, Diabetes, Temperature, And Other Parameters. Drug Recommendation Systems Provide Precise Information At Any Time While Improving The Performance, Integrity, And Privacy Of Patient Data In The Decision-making Process. Recommender System, The Decision Tree Produces The Most Accurate Results. In Times Of Medical Emergency, A Drug Recommendation System Is Helpful For Giving Patients Recommendations For Safe Medications.
Heart Disease Is One Of The Most Significant Causes Of Mortality In The World Today. Prediction Of Cardio Vascular Disease Is A Critical Challenge In The Area Of Clinical Data Analysis.Machine Learning(ML)has Been Shown To Be Effective In Assisting In Making Decisions And Predictions From The Large Quantity Of Data Produced By The Health Care Industry.We Have Also Seen ML Techniques Being Used In Recent Developments In Different Areas Of The Internet Of Things (IoT). Various Studies Give Only A Glimpse Into Predicting Heart Disease With ML Techniques. In This Paper, We Propose A Novel Method That Aims At finding Significant Features By Applying Machine Learning Techniques Resulting In Improving The Accuracy In The Prediction Of Cardiovascular Disease. The Prediction Model Is Introduced With Different Combinations Of Features And Several Known Classification Techniques.
The Bone Fracture Detection And Classification Project Is A Pioneering Endeavor In The Field Of Medical Imaging And Diagnostics. Leveraging Advanced Image Processing And Deep Learning Techniques, This Project Is Designed To Detect And Classify Bone Fractures Based On Uploaded Medical Images. Specifically, It Focuses On The Identification Of Fractures In The Elbow, Hand, And Shoulder Regions. The Bone Fracture Detection And Classification Project Represents A Significant Advancement In The Realm Of Medical Imaging, Offering A Cost-effective And Efficient Solution For The Early Detection And Accurate Classification Of Bone Fractures. With Its Focus On The Elbow, Hand, And Shoulder Regions, It Serves As A Valuable Tool For Medical Practitioners, Enabling Timely And Informed Decisions In Patient Care.
Hospital Readmissions Pose Additional Costs And Discomfort For The Patient And Their Occurrences Are Indicative Of Deficient Health Service Quality, Hence Efforts Are Generally Made By Medical Professionals In Order To Prevent Them. These Endeavors Are Especially Critical In The Case Of Chronic Conditions, Such As Diabetes. Recent Developments In Machine Learning Have Been Successful At Predicting Readmissions From The Medical History Of The Diabetic Patient. However, These Approaches Rely On A Large Number Of Clinical Variables Thereby Requiring Deep Learning Techniques. This Article Presents The Application Of Simpler Machine Learning Models Achieving Superior Prediction Performance While Making Computations More Tractable. Index Terms—diabetes, Hospital Readmission, Neural Network, Random Forest, Logistic Regression.
Is Human Eye Illness Which Occurs In Individuals Who Have Diabetics Which Harms Their Retina And In The Long Run, May Lead Visual Deficiency. Till Now DR Is Being Screened Manually By Ophthalmologist Which Is A Very Time Consuming Procedure. And Henceforth This Task (project) Focuses On Analysis Of Different DR Stages, Which Is Done With Deep Learning (DL) And It Is A Subset Of Artificial Intelligence (AI). We Trained A Model Called DenseNet On An Enormous Dataset Including Around 3662 Train Images To Automatically Detect The DR Stage And These Are Classified Into High Resolution Fundus Images. The Dataset Which Are Using Is Available On Kaggle (APTOS). There Are Five DR Stages, Which Are 0, 1, 2, 3, And 4. In This Paper Patient’s Fundus Eye Images Are Used As The Input Parameters. A Trained Model (DenseNet Architecture) Will Further Extract The Feature Of Fundus Images Of Eye And After That Activation Function Gives The Output. This Architecture Gave An Accuracy Of 0.9611 (quadratic Weighted Kappa Score Of 0.8981) To DR Detection. And In The End, We Are Comparing The Two CNN Architectures, Which Are VGG16 Architecture And DenseNet121 Architecture
Thyroid Disease Is A Major Cause Of Formation In Medical Diagnosis And In Theprediction, Onset To Which It Is A Difficult Axiomin The Medical Research. Thyroid Gland Is One Of The Most Important Organs In Our Body. The Secretions Of Thyroid Hormones Are Culpable In Controlling The Metabolism.Hyperthyroidism And Hypothyroidism Are One Of The Two Common Diseases Of The Thyroid That Releases Thyroid Hormones In Regulating The Rate Of Body’s Metabolism. Data Cleansing Techniques Were Applied To Make The Data Primitive Enough For Performing Analytics To Show The Risk Of Patients Obtaining Thyroid. The Machine Learning Plays A Decisive Role In The Process Of Disease Prediction And This Paper Handles The Analysis Andclassificationmodels That Are Being Used In The Thyroid Disease Based On The Information Gathered From The Dataset Taken From UCI Machine Learning Repository. It Is Important To Ensure A Decent Knowledge Base That Can Be Entrenched And Used As A Hybrid Model In Solving Complex Learning Task, Such As In Medical Diagnosis And Prognostic Tasks. In This Paper, We Also Proposed Different Machine Learning Techniques And Diagnosis For The Prevention Of Thyroid. Machine Learning Algorithms, Support Vector Machine (SVM), K-NN, Decision Trees Were Used To Predict The Estimated Risk On A Patient’s Chance Of Obtaining Thyroid Disease.
Hospital Readmissions Pose Additional Costs And Discomfort For The Patient And Their Occurrences Are Indicative Of Deficient Health Service Quality, Hence Efforts Are Generally Made By Medical Professionals In Order To Prevent Them. These Endeavors Are Especially Critical In The Case Of Chronic Conditions, Such As Diabetes. Recent Developments In Machine Learning Have Been Successful At Predicting Readmissions From The Medical History Of The Diabetic Patient. However, These Approaches Rely On A Large Number Of Clinical Variables Thereby Requiring Deep Learning Techniques. This Article Presents The Application Of Simpler Machine Learning Models Achieving Superior Prediction Performance While Making Computations More Tractable. Index Terms—diabetes, Hospital Readmission, Neural Network, Random Forest, Logistic Regression.
Liver, A Crucial Interior Organ Of The Human Body Whose Principal Tasks Are To Eliminate Generated Waste Produced By Our Organism, Digest Food, And Preserve Vitamins And Energy Materials. The Liver Disease Can Cause Various Fatal Diseases, Including Liver Cancer. Early Diagnosis, And Treating The Patients Are Compulsory To Reduce The Risk Of Those Lethal Diseases. As The Diagnosis Of Liver Disease Is Expensive And Sophisticated, Numerous Researches Have Been Performed Using Machine Learning (ML) Methods For Classifying Liver Disorder Cases. In This Paper, We Have Compared Four Different ML Algorithms Such As Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), And Nearest Neighbour Classifier (NN) , Support Vector Classifier (SVM), Gaussian Naïve Bayes (GNB) For Classifying Indian Liver Patient Dataset (ILPD). Pearson Correlation Coefficient Based Feature Selection (PCC-FS) Is Applied To Eliminate Irrelevant Features From The Dataset. The Comparative Analysis Is Evaluated In Terms Of Accuracy, ROC, F-1 Score, Precision, And Recall. After Comparing Experimental Results, We Have Found That Logistic Regression On ET Provides The Highest Accuracy Of 71.24 %..
Cervical Cancer Is One Of The Main Reasons Of Death From Cancer In Women. The Complication Of This Cancer Can Be Limited If It Is Diagnosed And Treated At An Early Stage. In This Paper, We Propose A Cervical Cancer Cell Detection And Classification System Based On Convolutional Neural Networks (CNNs). The Cell Images Are Fed Into A CNNs Model To Extract Deep-learned Features. Cervical Cancer Is Screened Using Visual Inspection After Application Of Acetic Acid (VIA), Papanicolaou (Pap) Test, Human Papillomavirus (HPV) Test And Histopathology Test. Inter- And Intra-observer Variability May Occur During The Manual Diagnosis Procedure, Resulting In Misdiagnosis. The Purpose Of This Study Was To Develop An Integrated And Robust System For Automatic Cervix Type And Cervical Cancer Classification Using Deep Learning Techniques.
Pneumonia Is A Prevalent And Life-threatening Respiratory Disease That Affects Individuals Of All Age Groups Worldwide. Timely And Accurate Diagnosis Is Crucial For Effective Patient Management And Treatment. In Recent Years, Deep Learning Techniques Have Shown Great Promise In Automating The Detection Of Pneumonia From Medical Images, Particularly X-ray Radiographs. This Study Focuses On The Development Of A Deep Learning-based System For Pneumonia Detection Using X-ray Images. The Primary Objective Of This Research Is To Design, Implement, And Evaluate A Robust And Accurate Deep Learning Model That Can Aid Medical Professionals In The Rapid And Reliable Identification Of Pneumonia. The Proposed Model Leverages Convolutional Neural Networks (CNNs) And State-of-the-art Deep Learning Architectures To Extract Relevant Features From X-ray Images. These Features Are Then Used For Binary Classification, Distinguishing Between Normal And Pneumonia-affected Cases. In Conclusion, This Study Presents A Comprehensive Exploration Of The Application Of Deep Learning In Pneumonia Detection Using X-ray Images, With The Aim Of Advancing The State Of The Art In Automated Diagnostic Tools For Pneumonia In The Medical Field.
This Study Is An Attempt To Understand And Address The Cancer Disease Prediction ,—Since, Cancer Is Curable When Diagnosed At An Early Stage, Lung Cancer Screening Plays An Important Role In Preventive Care. Although Both Low Dose Computed Tomography (LDCT) And Computed Tomography (CT) Scans Provide Greater Medical Information Than Normal Chest X-rays, Access To These Technologies In Rural Areas Is Very Limited. There Is A Recent Trend Toward Using Computer-aided Diagnosis (CADx) To Assist In The Screening And Diagnosis Of Cancer From Biomedical Images. In This Study, The 121-layer Convolutional Neural Networkalong With The Transfer Learning Scheme Is Explored As A Means Of Classifying Lung Cancer Using Chest Xray Images. The Model Was Trained On A Lung Nodule Dataset Before Training On The Lung Cancer Dataset To Alleviate The Problem Of Using A Small Dataset. The Proposed Model Yields 74.43±6.01% Of Mean Accuracy, 74.96±9.85% Of Mean Specificity, And 74.68±15.33% Of Mean Sensitivity. The Proposed Model Also Provides A Heatmap For Identifying The Location Of The Lung Nodule. These Findings Are Promising For Further Development Of Chest X-ray-based Lung Cancer Diagnosis Using The Deep Learning Approach. Moreover, They Solve The Problem Of A Small Dataset..
Pneumonia Is A Prevalent And Life-threatening Respiratory Disease That Affects Individuals Of All Age Groups Worldwide. Timely And Accurate Diagnosis Is Crucial For Effective Patient Management And Treatment. In Recent Years, Deep Learning Techniques Have Shown Great Promise In Automating The Detection Of Pneumonia From Medical Images, Particularly X-ray Radiographs. This Study Focuses On The Development Of A Deep Learning-based System For Pneumonia Detection Using X-ray Images. The Primary Objective Of This Research Is To Design, Implement, And Evaluate A Robust And Accurate Deep Learning Model That Can Aid Medical Professionals In The Rapid And Reliable Identification Of Pneumonia. The Proposed Model Leverages Convolutional Neural Networks (CNNs) And State-of-the-art Deep Learning Architectures To Extract Relevant Features From X-ray Images. These Features Are Then Used For Binary Classification, Distinguishing Between Normal And Pneumonia-affected Cases.
The Process Of Discovering Or Mining Information From A Huge Volume Of Data Is Known As Data Mining Technology. Today Data Mining Has Lots Of Application In Every Aspects Of Human Life. Applications Of Data Mining Are Wide And Diverse. Among This Health Care Is A Major Application Of Data Mining. Medical Field Has Get Benefited More From Data Mining. Heart Disease Is The Most Dangerous Life-threatening Chronic Disease Globally. The Objective Of The Work Is To Predicts The Occurrence Of Heart Disease Of A Patient Using Random Forest Algorithm. The Dataset Was Accessed From Kaggle Site. The Dataset Contains 303 Samples And 14 Attributes Are Taken For Features Of The Dataset. Then It Was Processed Using Python Open Access Software In Jupyter Notebook. The Datasets Are Classified And Processed Using Machine Learning Algorithm Random Forest. The Outcomes Of The Dataset Are Expressed In Terms Of Accuracy, Sensitivity And Specificity In Percentage. Using Random Forest Algorithm, We Obtained Accuracy Of 86.9% For Prediction Of Heart Disease With Sensitivity Value 90.6% And Specificity Value 82.7%. From The Receiver Operating Characteristics, We Obtained The Diagnosis Rate For Prediction Of Heart Disease Using Random Forest Is 93.3%. The Random Forest Algorithm Has Proven To Be The Most Efficient Algorithm For Classification Of Heart Disease And Therefore It Is Used In The Proposed System.
Falling Is A Significant Health Problem. Fall Detection, To Alert For Medical Attention, Has Been Gaining Increasing Attention. Still, Most Of The Existing Studies Use Falls Simulated In A Laboratory Environment To Test The Obtained Performance. We Analyzed The Acceleration Signals Recorded By An Inertial Sensor On The Lower Back During 143 Real-world Falls (the Most Extensive Collection To Date) From The FARSEEING Repository. Such Data Were Obtained From Continuous Real-world Monitoring Of Subjects With A Moderate-to-high Risk Of Falling. We Designed And Tested Fall Detection Algorithms Using Features Inspired By A Multiphase Fall Model And A Machine Learning Approach Such As SVM And Decision Tree. The Obtained Results Suggest That Algorithms Can Learn Effectively From Features Extracted From A Multiphase Fall Model, Consistently Overperforming More Conventional Features. The Most Promising Method (support Vector Machines And Features From The Multiphase Fall Model) Obtained A Sensitivity Higher Than 80%, A False Alarm Rate Per Hour Of 0.56, And An F-measure Of 64.6%. The Reported Results And Methodologies Represent An Advancement Of Knowledge On Real-world Fall Detection And Suggest Useful Metrics For Characterizing Fall Detection Systems For Real-world Use. The SVM And Decision Tree Has Implemented For The Classification Of The Fall.
Malaria Is The Deadliest Disease In The Earth And Big Hectic Work For The Health Department. The Traditional Way Of Diagnosing Malaria Is By Schematic Examining Blood Smears Of Human Beings For Parasite-infected Red Blood Cells Under The Microscope By Lab Or Qualified Technicians. This Process Is Inefficient And The Diagnosis Depends On The Experience And Well Knowledgeable Person Needed For The Examination. Deep Learning Algorithms Have Been Applied To Malaria Blood Smears For Diagnosis Before. However, Practical Performance Has Not Been Sufficient So Far. This Paper Proposes A New And Highly Robust Machine Learning Model Based On A Convolutional Neural Network (CNN) Which Automatically Classifies And Predicts Infected Cells In Thin Blood Smears On Standard Microscope Slides. A Ten-fold Cross-validation Layer Of The Convolutional Neural Network On 27,558 Single-cell Images Is Used To Understand The Parameter Of The Cell. Three Types Of CNN Models Are Compared Based On Their Accuracy And Select The Precise Accurate - Basic CNN, VGG-19 Frozen CNN, And VGG-19 Fine Tuned CNN. Then By Comparing The Accuracy Of The Three Models, The Model With A Higher Rate Of Accuracy Is Acquired.
Blood Cancer (Leukemia) Is One Of The Leading Causes Of Death Among Humans. The Pace Of Healing Depends Mainly On Early Detection And Diagnosis Of A Disease. The Main Reason Behind Occurrence Of Leukemia Is When Bone Marrow Produces A Lot Of Abnormal White Blood Cells This Happens. Microscopic Study On Images Is Done By Hematologists Who Make Use Of Human Blood Samples, From Which It Leads To The Requirement Of Following Methods, Which Are Microscopic Color Imaging, Image Segmentation, Clustering And Classification Which Allows Easy Identification Of Patients Suffering From This Disease. Microscopic Imaging Allows For Various Methods Of Detecting Blood Cancer In Visible And Immature White Blood Cells. Identifying Leukemia Early And Quickly Greatly Helps Practitioners In Providing Appropriate Treatment To Patients. Initially To Start With, Segmentation Stage Is Achieved By Segregating White Blood Cells From Other Blood Components .For Diagnosing Prediction Of Leukemia, Geometrical Features Such As Area, Perimeter Of The White Blood Cell Nucleuses Investigated. In The Proposed Methodology We Make Use Of K-means, For Identifying Cancerous Stages And Its Early Detection. Experimentation And Results Were Found To Be Promising With The Accuracy Of 90% Identification Of The Cancer Cells.
Breast Cancer Is More Common Hence, Identification Of BC And Detection Of Region Of Breast Affected Is More Important. Mammography Screening Images Two Views CC And MLO Are Widely Use In Diagnosis Process. This Paper Presents The Method To Detect Cancer Region And Classify Normal And Cancerous Patient. Pre-processing Operation Perform On The Input Mammogram Image And Undesirable Part Removed From The Image, Tumor Region Segmented From The Image Using Morphological Operation And Highlighted The Region On Original Mammogram Image Or If Mammogram Image Is Normal Case Then It Shows That Patient Is Normal. Random Forest (RF) Classifiers Is Used For Classification Of BC Patient And Normal Patient. Classification Accuracy Of RF Is 95% For Image Of Different Patient. Processing Time Of RF Classifier Is 6.25s.
Computer Aided Diagnosis (CAD) Is Quickly Evolving, Diverse Field Of Study In Medical Analysis. Significant Efforts Have Been Made In Recent Years To Develop Computer-aided Diagnostic Applications, As Failures In Medical Diagnosing Processes Can Result In Medical Therapies That Are Severely Deceptive. Machine Learning (ML) Is Important In Computer Aided Diagnostic Test. Object Such As Body-organs Cannot Be Identified Correctly After Using An Easy Equation. Therefore, Pattern Recognition Essentially Requires Training From Instances. In The Bio Medical Area, Pattern Detection And ML Promises To Improve The Reliability Of Disease Approach And Detection. They Also Respect The Dispassion Of The Method Of Decisions Making. ML Provides A Respectable Approach To Make Superior And Automated Algorithm For The Study Of High Dimension And Multi - Modal Bio Medicals Data. The Relative Study Of Various ML Algorithm For The Detection Of Various Disease Such As Heart Disease, Diabetes Disease Is Given In This Survey Paper. It Calls Focus On The Collection Of Algorithms And Techniques For ML Used For Disease Detection And Decision Making Processes.
The Outbreaks Of COVID-19 Virus Have Crossed The Limit To Our Expectation And It Breaks All Previous Records Of Virus Outbreaks. The Effect Of Corona Virus Causes A Serious Illness May Result In Death As A Consequence Of Substantial Alveolar Damage And Progressive Respiratory Failure. Automatic Detection And Classification Of This Virus From Chest X-ray Image Using Computer Vision Technology Can Be Very Useful Complement With Respect To The Less Sensitive Traditional Process Of Detecting COVID-19 I.e. Reverse Transcription Polymerase Chain Reaction (RT-PCR). This Automated Process Offers A Great Potential To Enhance The Conventional Healthcare Tactic For Tackling COVID-19 And Can Mitigate The Shortage Of Trained Physicians In Remote Communities. Again, The Segmentation Of The Infected Regions From Chest X-ray Image Can Help The Medical Specialists To View Insights Of The Affected Region.
Human Activity Recognition Has Attracted The Attention Of Researchers Around The World. This Is An Interesting Problem That Can Be Addressed In Different Ways. Many Approaches Have Been Presented During The Last Year .These Applications Present Solutions To Recognize Different Kinds Of Activities Such As If The Person Is Walking, Running, Jumping, Jogging, Or Falling, Among Others. Amongst All These Activities, Fall Detection Has Special Importance Because It Is A Common Dangerous Event For People Of All Ages With A More Negative Impact On The Elderly Population. Usually, These Applications Use Sensors To Detect Sudden Changes In The Movement Of The Person. These Kinds Of Sensors Can Be Embedded In Smartphones, Necklaces, Or Smart Wristbands To Make Them ‘‘wearable’’ Devices. The Main Inconvenience Is That These Devices Have To Be Placed On The Subjects’ Bodies. This Might Be Uncomfortable And Is Not Always Feasible Because This Type Of Sensor Must Be Monitored Constantly, And Can Not Be Used In Open Spaces With Unknown People.
Breast Cancer Is More Common Hence, Identification Of BC And Detection Of Region Of Breast Affected Is More Important. Mammography Screening Images Two Views CC And MLO Are Widely Use In Diagnosis Process. This Paper Presents The Method To Detect Cancer Region And Classify Normal And Cancerous Patient. Pre-processing Operation Perform On The Input Mammogram Image And Undesirable Part Removed From The Image, Tumor Region Segmented From The Image Using Morphological Operation And Highlighted The Region On Original Mammogram Image Or If Mammogram Image Is Normal Case Then It Shows That Patient Is Normal. Random Forest (RF) Classifiers Is Used For Classification Of BC Patient And Normal Patient. Classification Accuracy Of RF Is 95% For Image Of Different Patient. Processing Time Of RF Classifier Is 6.25s.
Blood Cancer (Leukemia) Is One Of The Leading Causes Of Death Among Humans. The Pace Of Healing Depends Mainly On Early Detection And Diagnosis Of A Disease. The Main Reason Behind Occurrence Of Leukemia Is When Bone Marrow Produces A Lot Of Abnormal White Blood Cells This Happens. Microscopic Study On Images Is Done By Hematologists Who Make Use Of Human Blood Samples, From Which It Leads To The Requirement Of Following Methods, Which Are Microscopic Color Imaging, Image Segmentation, Clustering And Classification Which Allows Easy Identification Of Patients Suffering From This Disease. Microscopic Imaging Allows For Various Methods Of Detecting Blood Cancer In Visible And Immature White Blood Cells. Identifying Leukemia Early And Quickly Greatly Helps Practitioners In Providing Appropriate Treatment To Patients. Initially To Start With, Segmentation Stage Is Achieved By Segregating White Blood Cells From Other Blood Components .For Diagnosing Prediction Of Leukemia, Geometrical Features Such As Area, Perimeter Of The White Blood Cell Nucleuses Investigated. In The Proposed Methodology We Make Use Of K-means, For Identifying Cancerous Stages And Its Early Detection. Experimentation And Results Were Found To Be Promising With The Accuracy Of 90% Identification Of The Cancer Cells.
Oral Cancer Is A Major Global Health Issue Accounting For 177,384 Deaths In 2018 And It Is Most Prevalent In Low- And Middle-income Countries. Enabling Automation In The Identification Of Potentially Malignant And Malignant Lesions In The Oral Cavity Would Potentially Lead To Low-cost And Early Diagnosis Of The Disease. Building A Large Library Of Well-annotated Oral Lesions Is Key. As Part Of The MeMoSA® (Mobile Mouth Screening Anywhere) Project, Images Are Currently In The Process Of Being Gathered From Clinical Experts From Across The World, Who Have Been Provided With An Annotation Tool To Produce Rich Labels.
Heartdiseaseisoneofthemostsignificantcausesofmortalityintheworldtoday.Predictionof Cardio Vascular Disease Is A Critical Challenge In The Area Of Clinical Data Analysis.Machine Learning(ML)has Been Shown To Be Effective In Assisting In Making Decisions And Predictions From The Large Quantity Of Data Produced By The Health Care Industry.We Have Also Seen ML Techniques Being Used In Recent Developments In Different Areas Of The Internet Of Things (IoT). Various Studies Give Only A Glimpse Into Predicting Heart Disease With ML Techniques. In This Paper, We Propose A Novel Method That Aims At finding Significant Features By Applying Machine Learning Techniques Resulting In Improving The Accuracy In The Prediction Of Cardiovascular Disease. The Prediction Model Is Introduced With Different Combinations Of Features And Several Known Classification Techniques
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