import pandas as pd

Chronic kidney disease (CKD)

df = pd.read_csv("ckd.csv")
df

	Age	Blood Pressure	Specific Gravity	Albumin	Sugar	Red Blood Cells	Pus Cell	Pus Cell clumps	Bacteria	Blood Glucose Random	...	Packed Cell Volume	White Blood Cell Count	Red Blood Cell Count	Hypertension	Diabetes Mellitus	Coronary Artery Disease	Appetite	Pedal Edema	Anemia	Class
0	48	70	1.005	4	0	normal	abnormal	present	notpresent	117	...	32	6700	3.9	yes	no	no	poor	yes	yes	1
1	53	90	1.020	2	0	abnormal	abnormal	present	notpresent	70	...	29	12100	3.7	yes	yes	no	poor	no	yes	1
2	63	70	1.010	3	0	abnormal	abnormal	present	notpresent	380	...	32	4500	3.8	yes	yes	no	poor	yes	no	1
3	68	80	1.010	3	2	normal	abnormal	present	present	157	...	16	11000	2.6	yes	yes	yes	poor	yes	no	1
4	61	80	1.015	2	0	abnormal	abnormal	notpresent	notpresent	173	...	24	9200	3.2	yes	yes	yes	poor	yes	yes	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
153	55	80	1.020	0	0	normal	normal	notpresent	notpresent	140	...	47	6700	4.9	no	no	no	good	no	no	0
154	42	70	1.025	0	0	normal	normal	notpresent	notpresent	75	...	54	7800	6.2	no	no	no	good	no	no	0
155	12	80	1.020	0	0	normal	normal	notpresent	notpresent	100	...	49	6600	5.4	no	no	no	good	no	no	0
156	17	60	1.025	0	0	normal	normal	notpresent	notpresent	114	...	51	7200	5.9	no	no	no	good	no	no	0
157	58	80	1.025	0	0	normal	normal	notpresent	notpresent	131	...	53	6800	6.1	no	no	no	good	no	no	0

158 rows × 25 columns

df = df[["Hemoglobin", "Blood Glucose Random", "White Blood Cell Count", "Class"]].copy()
df

	Hemoglobin	Blood Glucose Random	White Blood Cell Count	Class
0	11.2	117	6700	1
1	9.5	70	12100	1
2	10.8	380	4500	1
3	5.6	157	11000	1
4	7.7	173	9200	1
...	...	...	...	...
153	15.7	140	6700	0
154	16.5	75	7800	0
155	15.8	100	6600	0
156	14.2	114	7200	0
157	15.8	131	6800	0

158 rows × 4 columns

df["Hemoglobin_su"] = (df["Hemoglobin"] - df["Hemoglobin"].mean()) / df["Hemoglobin"].std(ddof=0)
df["Glucose_su"] = (df["Blood Glucose Random"] - df["Blood Glucose Random"].mean()) / df["Blood Glucose Random"].std(ddof=0)
df["WhiteBCC_su"] = (df["White Blood Cell Count"] - df["White Blood Cell Count"].mean()) / df["White Blood Cell Count"].std(ddof=0)
df = df[["Hemoglobin_su", "Glucose_su", "WhiteBCC_su", "Class"]].copy()
df

	Hemoglobin_su	Glucose_su	WhiteBCC_su	Class
0	-0.865744	-0.221549	-0.569768	1
1	-1.457446	-0.947597	1.162684	1
2	-1.004968	3.841231	-1.275582	1
3	-2.814879	0.396364	0.809777	1
4	-2.083954	0.643529	0.232293	1
...	...	...	...	...
153	0.700526	0.133751	-0.569768	0
154	0.978974	-0.870358	-0.216861	0
155	0.735332	-0.484162	-0.601850	0
156	0.178436	-0.267893	-0.409356	0
157	0.735332	-0.005280	-0.537686	0

158 rows × 4 columns

import matplotlib.pyplot as plt

yes_ckd_df = df[df["Class"] == 1]
no_ckd_df = df[df["Class"] == 0]

plt.scatter(x=yes_ckd_df["Hemoglobin_su"], y=yes_ckd_df["Glucose_su"], label="YES CKD" )
plt.scatter(x=no_ckd_df["Hemoglobin_su"], y=no_ckd_df["Glucose_su"], label="NO CKD" )

plt.xlabel("Hemoglobin")
plt.ylabel("Glucose")
plt.legend()

plt.show()

Checking new patient

new_patient = [0, 1.5]

yes_ckd_df = df[df["Class"] == 1]
no_ckd_df = df[df["Class"] == 0]

plt.scatter(x=new_patient[0], y=new_patient[1], color="red", label="Unknown")

plt.scatter(x=yes_ckd_df["Hemoglobin_su"], y=yes_ckd_df["Glucose_su"], label="YES CKD" )
plt.scatter(x=no_ckd_df["Hemoglobin_su"], y=no_ckd_df["Glucose_su"], label="NO CKD" )
plt.xlabel("Hemoglobin")
plt.ylabel("Glucose")
plt.legend()

plt.show()

Nearest neighbor

new_point = [0, 1.5]

Distance between new_point and labeled points

import math as m

def euclide_distance(point1_x, point1_y, point2_x, point2_y):
    temp = (point1_x - point2_x)**2 + (point1_y - point2_y)**2
    return m.sqrt(temp)

distances_to_new_patient = []

for index, row in df.iterrows():
    point1_x = row["Hemoglobin_su"]
    point1_y = row["Glucose_su"]
    
    distance = euclide_distance(point1_x, point1_y, new_point[0], new_point[1])
    
    distances_to_new_patient.append(distance)

df["Distance"] = distances_to_new_patient

# obtain 10 cloest points
df = df.sort_values(["Distance"], ascending=True)
closest_points = df.head(10)
closest_points

	Hemoglobin_su	Glucose_su	WhiteBCC_su	Class	Distance
6	-0.413266	2.049282	0.360623	1	0.687386
13	-0.239236	2.296447	0.424788	1	0.831602
14	0.839750	1.215099	1.291014	1	0.886763
32	-0.865744	1.863908	5.750474	1	0.939118
35	-0.970162	1.276890	-0.345191	1	0.995486
34	-0.970162	1.879356	0.103963	1	1.041693
15	-1.144192	1.354130	-0.922675	1	1.153453
8	-1.109386	1.879356	-0.409356	1	1.172454
84	-0.030400	0.087407	-0.184779	0	1.412920
152	0.143630	0.087407	0.328540	0	1.419876

# find the most common "class" in the cloest points
closest_points["Class"].mode().values[0]

1

new_patient = [0, 1.5]

yes_ckd_df = closest_points[closest_points["Class"] == 1]
no_ckd_df = closest_points[closest_points["Class"] == 0]

plt.scatter(x=new_patient[0], y=new_patient[1], color="red", label="Unknown")

plt.scatter(x=yes_ckd_df["Hemoglobin_su"], y=yes_ckd_df["Glucose_su"], label="YES CKD" )
plt.scatter(x=no_ckd_df["Hemoglobin_su"], y=no_ckd_df["Glucose_su"], label="NO CKD" )
plt.xlabel("Hemoglobin")
plt.ylabel("Glucose")
plt.legend()

plt.show()

Decision boundary

def classify(reference_points, new_point, k_neighbors=1):
    distances_to_new_patient = []

    for index, row in df.iterrows():
        point1_x = row["Hemoglobin_su"]
        point1_y = row["Glucose_su"]

        distance = euclide_distance(point1_x, point1_y, new_point[0], new_point[1])

        distances_to_new_patient.append(distance)

    reference_points["Distance"] = distances_to_new_patient
    reference_points = reference_points.sort_values(["Distance"], ascending=True)
    closest_points = reference_points.head(k_neighbors)
    
    predicted_label = closest_points["Class"].mode().values[0]
    return predicted_label
    

import numpy as np
import matplotlib.pyplot as plt

yes_ckd_df = df[df["Class"] == 1]
no_ckd_df = df[df["Class"] == 0]

hemoglobins = np.arange(-2, 2, 0.1)
glucoses = np.arange(-2, 2, 0.1)

for h in hemoglobins:
    for g in glucoses:
        new_point = [h, g]
        predicted_val = classify(df, new_point, k_neighbors=1)
        if predicted_val == 1:
            plt.scatter(x=h, y=g, color="blue", alpha=0.2)
        else:
            plt.scatter(x=h, y=g, color="orange", alpha=0.2)

plt.scatter(x=yes_ckd_df["Hemoglobin_su"], y=yes_ckd_df["Glucose_su"], color="blue", label="YES CKD" )
plt.scatter(x=no_ckd_df["Hemoglobin_su"], y=no_ckd_df["Glucose_su"], color="orange", label="NO CKD" )
plt.xlabel("Hemoglobin")
plt.ylabel("Glucose")
plt.legend()

plt.show()