PK r\_rels/PK r\ docProps/PK r\ppt/PK r\ ppt/_rels/PK r\ ppt/charts/PK r\ppt/charts/_rels/PK r\ppt/embeddings/PK r\ ppt/media/PK r\ppt/slideLayouts/PK r\ppt/slideLayouts/_rels/PK r\ppt/slideMasters/PK r\ppt/slideMasters/_rels/PK r\ ppt/slides/PK r\ppt/slides/_rels/PK r\ ppt/theme/PK r\ppt/notesMasters/PK r\ppt/notesMasters/_rels/PK r\ppt/notesSlides/PK r\ppt/notesSlides/_rels/PK r\ rp[Content_Types].xml PK r\]] _rels/.rels PK r\!docProps/app.xml 0 0 Microsoft Office PowerPoint On-screen Show (16:9) 0 8 8 0 0 false Fonts Used 2 Theme 1 Slide Titles 8 Arial Calibri Office Theme Slide 1Slide 2Slide 3Slide 4Slide 5Slide 6Slide 7Slide 8 PptxGenJS false false false 16.0000 PK r\(..docProps/core.xml Healthcare Anomaly Detection - Technical Deep Dive PptxGenJS Presentation DATA 110 - UNC Chapel Hill DATA 110 - UNC Chapel Hill 1 2026-03-18T20:53:23Z 2026-03-18T20:53:23Z PK r\g ppt/_rels/presentation.xml.rels PK r\Oݨ ppt/theme/theme1.xmlPK r\ڧ ppt/presentation.xml PK r\Xppt/presProps.xml PK r\ppt/tableStyles.xml PK r\D >00ppt/viewProps.xml PK r\H7t!ppt/slideLayouts/slideLayout1.xml PK r\ђ77,ppt/slideLayouts/_rels/slideLayout1.xml.rels PK r\85tppt/slides/slide1.xml Anomaly Detectionin Patient Vital SignsTechnical Deep Dive: Models, Features, and Evaluation MetricsFor: Data Science / Engineering Team | DATA 110 ProjectSchool of Data Science and Society • UNC Chapel HillPK r\>DXX ppt/slides/_rels/slide1.xml.rels PK r\.ppt/notesSlides/notesSlide1.xml 1PK r\:A*ppt/notesSlides/_rels/notesSlide1.xml.rels PK r\3ppt/slides/slide2.xml Dataset OverviewFeature Set (8 Vital Signs) Feature Unit Normal Range Heart Rate bpm 60 – 100 Systolic BP mmHg 90 – 140 Diastolic BP mmHg 60 – 90 Oxygen Saturation % 95 – 100 Temperature °F 97.0 – 99.5 Respiratory Rate bpm 12 – 20 White Blood Cells K/μL 4.5 – 11.0 Glucose mg/dL 70 – 140 Class Distribution n = 500 | Imbalanced dataset (12% anomaly rate) | class_weight='balanced' used to handle imbalance | 70/30 train-test split with stratificationPK r\9\XX ppt/slides/_rels/slide2.xml.rels PK r\ppt/notesSlides/notesSlide2.xml 2PK r\xշ*ppt/notesSlides/_rels/notesSlide2.xml.rels PK r\~&``ppt/slides/slide3.xml Methodology: Three Approaches Random ForestSupervisedn_estimators=100max_depth=10class_weight='balanced'How it works:Ensemble of 100 decision trees. Each tree votes on whether a patient is normal or anomalous. Majority rules.+ Highest accuracy; interpretable feature importance− Requires labeled training data Isolation ForestUnsupervisedn_estimators=100contamination=0.12How it works:Randomly partitions feature space. Anomalies are isolated in fewer splits (shorter path length).+ No labels needed; scales well− Can't identify anomaly type DBSCANUnsupervisedeps=2.5min_samples=10How it works:Finds dense clusters in feature space. Points not belonging to any cluster are flagged as anomalies (noise).+ Discovers natural groupings; flexible shapes− Sensitive to eps/min_samples tuningPK r\W/ ppt/slides/_rels/slide3.xml.rels PK r\K |Őppt/notesSlides/notesSlide3.xml 3PK r\9 Y*ppt/notesSlides/_rels/notesSlide3.xml.rels PK r\dz#>..ppt/slides/slide4.xml Model Evaluation Metrics Metric DefinitionsAccuracyOverall correct predictions / total predictionsPrecisionOf those flagged anomaly, how many truly are? (Avoid false alarms)RecallOf all true anomalies, how many did we catch? (Don't miss critical cases)F1 ScoreHarmonic mean of Precision and Recall — balanced measure⚠ In healthcare, RECALL is critical — missing an anomaly (False Negative) can cost a life. Prioritize recall over precision.PK r\LXX ppt/slides/_rels/slide4.xml.rels PK r\vsppt/notesSlides/notesSlide4.xml 4PK r\J *ppt/notesSlides/_rels/notesSlide4.xml.rels PK r\˰ppt/slides/slide5.xml Feature Importance Analysis InterpretationTop 3 predictors account for 57% of model's decision power.Heart Rate is the strongest signal — elevated in sepsis and cardiac events, depressed in hypothermia.Temperature and WBC are classic infection indicators, explaining their high ranking.Blood pressure features rank lower — they differ across anomaly types, reducing their individual discriminative power.PK r\+hXX ppt/slides/_rels/slide5.xml.rels PK r\W8ppt/notesSlides/notesSlide5.xml 5PK r\Qe*ppt/notesSlides/_rels/notesSlide5.xml.rels PK r\yqMMppt/slides/slide6.xml Data Preprocessing Pipeline1Load & Inspectdf = pd.read_excel('data.xlsx')df.info() # Check types, nulls500 rows × 13 cols, no missing values2Feature Selectionfeatures = ['Heart_Rate_bpm', 'Systolic_BP_mmHg', ...] # 8 vitalsExcluded: Patient_ID, Timestamp, Gender (categorical)3Train/Test SplitX_train, X_test = train_test_split( X, y, test_size=0.30, stratify=y)Stratified split preserves 12% anomaly ratio in both sets4StandardScalerscaler = StandardScaler()X_train = scaler.fit_transform(X_train)X_test = scaler.transform(X_test)Fit on train only — prevents data leakagePK r\ج+ ppt/slides/_rels/slide6.xml.rels PK r\zppt/notesSlides/notesSlide6.xml 6PK r\=|*ppt/notesSlides/_rels/notesSlide6.xml.rels PK r\I!i/i/ppt/slides/slide7.xml Technical Lessons & Next Steps Lessons Learnedclass_weight='balanced' is essential for imbalanced anomaly datasetsStandardScaler must be fit on training data only to prevent data leakageDBSCAN eps parameter requires experimentation — too small = everything is noiseIsolation Forest contamination should approximate true anomaly rateSupervised models outperform unsupervised when quality labels exist Potential ImprovementsAdd temporal features (vital sign trends over time, rate of change)Try XGBoost or neural networks for higher accuracyImplement SMOTE oversampling as alternative to class_weightBuild a real-time streaming pipeline (Kafka → model → alert)Autoencoder for unsupervised — learns normal pattern, flags reconstruction errorsCross-validation (k=5) for more robust metric estimatesPK r\F ppt/slides/_rels/slide7.xml.rels PK r\)lppt/notesSlides/notesSlide7.xml 7PK r\|g*ppt/notesSlides/_rels/notesSlide7.xml.rels PK r\J/Fppt/slides/slide8.xml Questions?Let's Dive Into the Code.Jupyter Notebook + Excel Dataset Available for Hands-On ExplorationDATA 110 • School of Data Science and Society • UNC Chapel HillPK r\WsXX ppt/slides/_rels/slide8.xml.rels PK r\iސppt/notesSlides/notesSlide8.xml 8PK r\pO*ppt/notesSlides/_rels/notesSlide8.xml.rels PK r\K !ppt/slideMasters/slideMaster1.xml PK r\N),ppt/slideMasters/_rels/slideMaster1.xml.rels PK r\6TT!ppt/notesMasters/notesMaster1.xml 7/23/19Click to edit Master text stylesSecond levelThird levelFourth levelFifth level‹#›PK r\s **,ppt/notesMasters/_rels/notesMaster1.xml.rels PK r\^^appt/media/image-1-1.pngPNG  IHDR\rf pHYs  MIDATxi\E/[ cA0.%(0JQ R([D)P1\@-@J-! 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SU˼OZ軯[E2Iߔ5p ~"ǕŪo25n|ٯX[|?H>|mLX$87i5wnX86xWJ:n?.. . ºNt7o+^|8 xgXx*QQV7kynm&֟[DDDDDDDDDDDDDDDDDDDDDDDDDDD &sIENDB`PK r\6xBB.ppt/embeddings/Microsoft_Excel_Worksheet1.xlsxPK r\_rels/PK r\ docProps/PK r\xl/PK r\ xl/_rels/PK r\ xl/tables/PK r\ xl/theme/PK r\xl/worksheets/PK r\xl/worksheets/_rels/PK r\ ##[Content_Types].xml PK r\KK _rels/.rels PK r\*docProps/app.xmlMicrosoft Macintosh Excel0falseWorksheets1Sheet1falsefalsefalse16.0300 PK r\N m iidocProps/core.xmlPptxGenJSPptxGenJS2026-03-18T20:53:23.977Z2026-03-18T20:53:23.978ZPK r\ՙxl/_rels/workbook.xml.relsPK r\<]] xl/styles.xml PK r\^xl/theme/theme1.xmlPK r\Txl/workbook.xml PK r\I3j$$#xl/worksheets/_rels/sheet1.xml.rels PK r\l&yLLxl/sharedStrings.xmlClassNormal (88%)Sepsis (4%)Cardiac (4%)Hypothermia (4%) PK r\?w-xl/tables/table1.xml
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PK r\*kCCxl/worksheets/sheet1.xml01234958278590655569278707917162 PK r\_rels/PK r\ $docProps/PK r\Kxl/PK r\ lxl/_rels/PK r\ xl/tables/PK r\ xl/theme/PK r\xl/worksheets/PK r\xl/worksheets/_rels/PK r\ ##@[Content_Types].xmlPK r\KK _rels/.relsPK r\* docProps/app.xmlPK r\.iiP docProps/core.xmlPK r\ՙxl/_rels/workbook.xml.relsPK r\<]] xl/styles.xmlPK r\^`xl/theme/theme1.xmlPK r\T1xl/workbook.xmlPK r\I3j$$#3xl/worksheets/_rels/sheet1.xml.relsPK r\\IvvO5xl/sharedStrings.xmlPK r\ ,6xl/tables/table1.xmlPK r\*kCC!9xl/worksheets/sheet1.xmlPK>PK r\{*>> ppt/charts/_rels/chart2.xml.relsPK r\ Jppt/charts/chart2.xml Sheet1!$B$1 Random Forest Sheet1!$A$2:$A$5 AccuracyPrecisionRecallF1 Score Sheet1!$B$2:$B$5 General 95909291 Sheet1!$C$1 Isolation Forest Sheet1!$A$2:$A$5 AccuracyPrecisionRecallF1 Score Sheet1!$C$2:$C$5 General 82657871 Sheet1!$D$1 DBSCAN Sheet1!$A$2:$A$5 AccuracyPrecisionRecallF1 Score Sheet1!$D$2:$D$5 General 78557062 PK r\gCC.ppt/embeddings/Microsoft_Excel_Worksheet3.xlsxPK r\_rels/PK r\ docProps/PK r\xl/PK r\ xl/_rels/PK r\ xl/tables/PK r\ xl/theme/PK r\xl/worksheets/PK r\xl/worksheets/_rels/PK r\ ##[Content_Types].xml PK r\KK _rels/.rels PK r\*docProps/app.xmlMicrosoft Macintosh Excel0falseWorksheets1Sheet1falsefalsefalse16.0300 PK r\}{iidocProps/core.xmlPptxGenJSPptxGenJS2026-03-18T20:53:23.980Z2026-03-18T20:53:23.980ZPK r\ՙxl/_rels/workbook.xml.relsPK r\<]] xl/styles.xml PK r\^xl/theme/theme1.xmlPK r\Txl/workbook.xml PK r\I3j$$#xl/worksheets/_rels/sheet1.xml.rels PK r\Z!xl/sharedStrings.xmlImportanceHeart RateTemperatureWBC CountRespiratory RateOxygen Sat.GlucoseSystolic BPDiastolic BP PK r\^Sxl/tables/table1.xml
PK r\ұ xl/worksheets/sheet1.xml0120.2230.1940.1650.1360.1170.0880.0690.05 PK r\_rels/PK r\ $docProps/PK r\Kxl/PK r\ lxl/_rels/PK r\ xl/tables/PK r\ xl/theme/PK r\xl/worksheets/PK r\xl/worksheets/_rels/PK r\ ##@[Content_Types].xmlPK r\KK _rels/.relsPK r\* docProps/app.xmlPK r\}{iiP docProps/core.xmlPK r\ՙxl/_rels/workbook.xml.relsPK r\<]] xl/styles.xmlPK r\^`xl/theme/theme1.xmlPK r\T1xl/workbook.xmlPK r\I3j$$#3xl/worksheets/_rels/sheet1.xml.relsPK r\Z!O5xl/sharedStrings.xmlPK r\^S87xl/tables/table1.xmlPK r\ұ 9xl/worksheets/sheet1.xmlPK>PK r\u >> ppt/charts/_rels/chart3.xml.relsPK r\.9ppt/charts/chart3.xml Sheet1!$B$1 Importance Sheet1!$A$2:$A$9 Heart RateTemperatureWBC CountRespiratory RateOxygen Sat.GlucoseSystolic BPDiastolic BP Sheet1!$B$2:$B$9 General 0.220.190.160.130.110.080.060.05 PK r\_rels/PK r\ $docProps/PK r\Kppt/PK r\ mppt/_rels/PK r\ ppt/charts/PK r\ppt/charts/_rels/PK r\ppt/embeddings/PK r\ ppt/media/PK r\Bppt/slideLayouts/PK r\qppt/slideLayouts/_rels/PK r\ppt/slideMasters/PK r\ppt/slideMasters/_rels/PK r\  ppt/slides/PK r\3ppt/slides/_rels/PK r\ bppt/theme/PK r\ppt/notesMasters/PK r\ppt/notesMasters/_rels/PK r\ppt/notesSlides/PK r\ppt/notesSlides/_rels/PK r\ rpP[Content_Types].xmlPK r\]] (_rels/.relsPK r\!docProps/app.xmlPK r\(.."docProps/core.xmlPK r\g O&ppt/_rels/presentation.xml.relsPK r\Oݨ .ppt/theme/theme1.xmlPK r\ڧ Oppt/presentation.xmlPK r\Xe]ppt/presProps.xmlPK r\^ppt/tableStyles.xmlPK r\D >00_ppt/viewProps.xmlPK r\H7t!bppt/slideLayouts/slideLayout1.xmlPK r\ђ77,eppt/slideLayouts/_rels/slideLayout1.xml.relsPK r\85thgppt/slides/slide1.xmlPK r\>DXX Vppt/slides/_rels/slide1.xml.relsPK r\.ppt/notesSlides/notesSlide1.xmlPK r\:A*ppt/notesSlides/_rels/notesSlide1.xml.relsPK r\3̋ppt/slides/slide2.xmlPK r\9\XX Mppt/slides/_rels/slide2.xml.relsPK r\Pppt/notesSlides/notesSlide2.xmlPK r\xշ*XWppt/notesSlides/_rels/notesSlide2.xml.relsPK r\~&``kYppt/slides/slide3.xmlPK r\W/ Kppt/slides/_rels/slide3.xml.relsPK r\K |ŐWppt/notesSlides/notesSlide3.xmlPK r\9 Y*$ppt/notesSlides/_rels/notesSlide3.xml.relsPK r\dz#>..7ppt/slides/slide4.xmlPK r\LXX kppt/slides/_rels/slide4.xml.relsPK r\vsppt/notesSlides/notesSlide4.xmlPK r\J *ppt/notesSlides/_rels/notesSlide4.xml.relsPK r\˰ppt/slides/slide5.xmlPK r\+hXX ppt/slides/_rels/slide5.xml.relsPK r\W8Zppt/notesSlides/notesSlide5.xmlPK r\Qe*'"ppt/notesSlides/_rels/notesSlide5.xml.relsPK r\yqMM:$ppt/slides/slide6.xmlPK r\ج+ qppt/slides/_rels/slide6.xml.relsPK r\ztppt/notesSlides/notesSlide6.xmlPK r\=|*zppt/notesSlides/_rels/notesSlide6.xml.relsPK r\I!i/i/|ppt/slides/slide7.xmlPK r\F ppt/slides/_rels/slide7.xml.relsPK r\)lppt/notesSlides/notesSlide7.xmlPK r\|g*]ppt/notesSlides/_rels/notesSlide7.xml.relsPK r\J/Fpppt/slides/slide8.xmlPK r\WsXX ppt/slides/_rels/slide8.xml.relsPK r\iސ<ppt/notesSlides/notesSlide8.xmlPK r\pO* ppt/notesSlides/_rels/notesSlide8.xml.relsPK r\K !ppt/slideMasters/slideMaster1.xmlPK r\N),ppt/slideMasters/_rels/slideMaster1.xml.relsPK r\6TT!ppt/notesMasters/notesMaster1.xmlPK r\s **,ppt/notesMasters/_rels/notesMaster1.xml.relsPK r\^^appt/media/image-1-1.pngPK r\^^a'ppt/media/image-8-1.pngPK r\6xBB.8ppt/embeddings/Microsoft_Excel_Worksheet1.xlsxPK r\(ǰ>> zppt/charts/_rels/chart1.xml.relsPK r\8?qq{|ppt/charts/chart1.xmlPK r\lăCC.ppt/embeddings/Microsoft_Excel_Worksheet2.xlsxPK r\{*>> ppt/charts/_rels/chart2.xml.relsPK r\ Jjppt/charts/chart2.xmlPK r\gCC.wppt/embeddings/Microsoft_Excel_Worksheet3.xlsxPK r\u >> 9ppt/charts/_rels/chart3.xml.relsPK r\.9;ppt/charts/chart3.xmlPKNNSO