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The second project for my university ML learning progress, let's review some ML technique for real quick

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lovecrushfamily/Alzheimer_Disease

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Alzheimer Disease

Author : LoveCrush
Time : Within a week
Collaborator : Than. Ng
Goal : Review tools and models for ML
Dataset : Alzheimer Disease

You can follow this link to get fully intuition about this dataset Alzheimer Disease - Kaggle Dataset

Motivation

This project's considering as a second project for my uni learning progress. 'Cuz I'm not have any project doing on ML so far. This project was created for that purpose. Just a small project but I'm gonna try my best to apply all of my knowledge into this. So let's briefly describe this dataset for real quick.

Dataset

Features (cols) : 35
Samples (rows) : 2149

S.No Name Not Null Dtype Description
Demographic Detail
0 Age 2149 non-null int64 -> numerical Age of patient ranges from 60 -> 90 years
1 Gender 2149 non-null int64 -> categorical Gender of the patients, O represents Male and vice versa
2 Ethnicity 2149 non-null int64 -> categorical The ethnicity of the patients, already encoded (irrelevant in detail)
3 EducationLevel 2149 non-null int64 -> categorical The educational level of patient, already encoded (irrelevant in detail)
Lifestyle Factor
4 BMI 2149 non-null float64 -> numerical Body Mass Index of the patients, ranging from 15 -> 40
5 Smoking 2149 non-null int64 -> categorical Smoking status, where 0 indicates No and 1 indicates Yes
6 AlcoholConsumption 2149 non-null float64 -> numerical Weekly alcohol consumption in units, ranging from 0 -> 20
7 PhysicalActivity 2149 non-null float64 -> numerical Weekly physical activity in hours, ranging from 0 -> ten
8 DietQuality 2149 non-null float64 -> numerical Diet quality score, ranging from 0 -> 10
9 SleepQuality 2149 non-null float64 -> numerical Sleep quality score, ranging from 4 -> 10
Mediacal Hostory
10 FamilyHistoryAlzheimers 2149 non-null int64 -> categorical Family history of Alzheimer's Disease, 0 indicated No and 1 indicated Yes
11 CardiovascularDisease 2149 non-null int64 -> categorical Presence of Cardiovascular Disease, 0 indicates No, 1 indicates Yes
12 Diabetes 2149 non-null int64 -> categorical Presence of diabetes, 0 indicates No and 1 indicates Yes
13 Depression 2149 non-null int64 -> categorical Presence of Depression, where 0 indicates No and 1 indicates Yes
14 HeadInjury 2149 non-null int64 -> categorical History of head injury, 0 indicates No and 1 indicates Yes
15 Hypertension 2149 non-null int64 -> categorical Presence of Hypertension, 0 indicates No and 1 indicates Yes
Clinical Measurements
16 SystolicBP 2149 non-null int64 -> numerical Systolic blood pressure, ranging from 90 -> 180 mmHg
17 DiastolicBP 2149 non-null int64 -> numerical Diastolic blood pressure, ranging from 60 to 120 mmHg
18 CholesterolTotal 2149 non-null float64 -> numerical Total Cholesterol levels, ranging from 150 -> 300 mm/dL
19 CholesterolLDL 2149 non-null float64 -> numerical Low-density lipoprotein cholesterol levels, ranging from 50 -> 200 mg/dL
20 CholesterolHDL 2149 non-null float64 -> numerical High-density lipoprotein cholesterol levels, ranging from 20 -> 100 mm/dL
21 CholesterolTriglycerides 2149 non-null float64 -> numerical Triglycerides levels, ranging from 50 -> 400 mg/dL.
Cognitive and Functional Assessments
22 MMSE 2149 non-null float64 -> numerical Mini-Mental State Examination score, ranging from 0 -> 30, lower scores indicate cognitive impairment
23 FunctionalAssessment 2149 non-null float64 -> numerical Functional assessment score, ranging from 0 -> 10, lower scores indicate greater impairment
24 MemoryComplaints 2149 non-null int64 -> categorical Presence of Memory complaints, where 0 indicates No and 1 indicates Yes
25 BehavioralProblems 2149 non-null int64 -> categorical Presence of Behavioral problem, where 0 indicates No and 1 indicate Yes
26 ADL 2149 non-null float64 -> numerical Activities of Daily Living score, ranging from 0 -> 10, lower scores indicate greater impairment
Symptoms
27 Confusion 2149 non-null int64 -> categorical Presence of confusion, 0 indicated No and 1 indicated Yes
28 Disorientation 2149 non-null int64 -> categorical Presence of disorientation, 0 indicates No and 1 indicates Yes
29 PersonalityChanges 2149 non-null int64 -> categorical Presence of personality changes, 0 indicates No and 1 indicates Yes
30 DifficultyCompletingTasks 2149 non-null int64 -> categorical Presence of difficulty completing tasks, where 0 indicates No and 1 indicates Yes
31 Forgetfulness 2149 non-null int64 -> categorical Presence of forgetfulness, where 0 indicates No and 1 indicates Yes
Diagnosis Information
32 Diagnosis 2149 non-null int64 -> categorical Diagnosis status for Alzheimer's Disease, where 0 indicates No and 1 indicates Yes
Confidential Information
33 DoctorInCharge 2149 non-null object -> categorical (irrelevant) The confidential information about the doctor in charge, already encoded as "XXXConfid for all patients
34 PatientID 2149 non-null int64 -> numerical (irrelevant) A unique identifier assigned to each patient

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The second project for my university ML learning progress, let's review some ML technique for real quick

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